JP7779338B2 - Evidence management device, evidence management system, evidence management method and program - Google Patents

Description

The present invention relates to an evidence management device, an evidence management system, an evidence management method, and a program .

Various technologies have been proposed for verifying identity using facial images.

For example, the identity verification and authentication system described in Patent Document 1 calculates a face score value when registering a user from biometric information generated based on a facial image captured by an imaging unit and biometric information generated based on a facial image included in an image of an identity document. If the face score value is equal to or greater than a first predetermined value, the identity verification and authentication system registers the biometric information generated based on the facial image captured by the imaging unit as registered biometric information.

Patent document 2 discloses a facial recognition device designed to prevent a decline in convenience at the start of operation. This facial recognition device includes a facial image acquisition unit, a matching unit, a time measurement unit, and a threshold change unit. The facial image acquisition unit acquires a facial image of the target for authentication. The matching unit performs facial recognition on the facial image of the target for authentication based on a threshold. The time measurement unit measures the elapsed time since the start of operation of the facial recognition device. The threshold change unit restricts the threshold from being changed to a value greater than a value determined depending on the measured elapsed time.

Japanese Patent Application Laid-Open No. 2021-125115 Japanese Patent Application Laid-Open No. 2019-125003

The identity verification and authentication system described in Patent Document 1 performs identity verification processing using a first predetermined value when registering a user. The identity verification and authentication system registers, as registered biometric information, biometric information generated based on a facial image captured by an imaging unit, from the biometric information used in the identity verification processing at the time of registration.

Information including facial images captured by the imaging unit described in Patent Document 1 may be saved as evidence for the identity verification process at the time of registration. However, facial images captured by the imaging unit may have poor image quality due to reasons such as being slightly out of focus. Facial images with poor image quality may cause problems, such as inability to perform sufficient verification if it becomes necessary to use the evidence after registration.

The present invention was made in consideration of the above-mentioned circumstances, and its purpose is to provide an evidence management device, etc. that enables the management of evidence with good image quality.

In order to achieve the above object, a trail management device according to a first aspect of the present invention comprises:
a data acquisition means for acquiring target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
a trail generating means for generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
The evidence generation means holds candidate data including a plurality of candidate values that are candidates for the second reference value, and repeatedly determines whether the clarity of the target image satisfies the second condition by using a candidate value selected from the plurality of candidate values in descending order as the second reference value .

In order to achieve the above object, a trail management system according to a second aspect of the present invention comprises:
The above-mentioned trail management device;
The device further comprises a terminal device that generates the target image data by photographing the person and the document and transmits the target image data to the trail management device.

In order to achieve the above object, a trail management method according to a third aspect of the present invention comprises:
The computer
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
Generating the evidence data includes repeatedly determining whether the clarity of the target image satisfies the second condition by using a candidate value selected from the plurality of candidate values in descending order as the second reference value based on candidate data including a plurality of candidate values that are candidates for the second reference value .

In order to achieve the above object, a program according to a fourth aspect of the present invention comprises:
On the computer,
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
The program for generating the evidence data includes repeatedly determining whether the clarity of the target image satisfies the second condition by using a candidate value selected from the plurality of candidate values in descending order as the second reference value based on candidate data including a plurality of candidate values that are candidates for the second reference value .

The present invention makes it possible to manage evidence of good image quality.

FIG. 1 is a diagram illustrating an example of the configuration of a trail management system according to a first embodiment of the present invention. FIG. 2 is a diagram showing an example of a target image P1. FIG. 2 is a diagram illustrating an example of a physical configuration of a terminal device according to the first embodiment of the present invention. FIG. 1 illustrates an example of the physical configuration of a trail management device according to a first embodiment of the present invention. 10 is a flowchart illustrating an example of a first terminal process according to the first exemplary embodiment of the present invention. 10 is a flowchart illustrating an example of a first server process according to the first exemplary embodiment of the present invention. FIG. 10 is a diagram illustrating an example of the configuration of a trail management system according to a second embodiment of the present invention. FIG. 10 illustrates an example of the functional configuration of a trail generation unit according to a second embodiment. 10 is a flowchart illustrating an example of a second terminal process according to the second exemplary embodiment of the present invention. FIG. 10 is a diagram showing an example of a preview image PP. 10 is a flowchart illustrating an example of a second server process according to the second exemplary embodiment of the present invention. FIG. 10 is a diagram illustrating an example of the configuration of trail data according to Modification 2. FIG. 10 is a diagram illustrating an example of the configuration of a trail management system according to a third embodiment of the present invention. FIG. 11 illustrates an example of the functional configuration of a trail generation unit according to a third embodiment. FIG. 10 is a diagram illustrating an example of candidate data. 13 is a flowchart illustrating an example of a third server process according to the third exemplary embodiment of the present invention. 13 is a flowchart illustrating an example of a fourth server process according to Modification 3. FIG. 10 is a diagram illustrating an example of the configuration of a trail management system according to a fourth embodiment of the present invention. FIG. 13 illustrates an example of the functional configuration of a trail generation unit according to a fourth embodiment. FIG. 10 is a diagram showing another example of candidate data. 13 is a flowchart illustrating an example of a fifth server process according to the fourth exemplary embodiment of the present invention. FIG. 10 is a diagram illustrating an example of the configuration of a trail management system according to a fifth embodiment of the present invention. 13 is a flowchart illustrating an example of a sixth server process according to the fifth exemplary embodiment of the present invention.

The following describes an embodiment of the present invention using the drawings. Note that in all drawings, similar components are designated by similar reference numerals, and descriptions will be omitted where appropriate.

<<First Embodiment>>
The trail management system 100 according to the first embodiment of the present invention is a system for managing trails of identity verification, and as shown in Fig. 1, includes a terminal device 101 and a server device 102 as a trail management device. The trail management device is a device for managing trails of identity verification.

The terminal device 101 and the server device 102 are connected via a network N and can send and receive data to and from each other via the network N. The network N is a communication network that is wired, wireless, or a combination of these.

Terminal device 101 is a terminal used by the person to be verified. Functionally, terminal device 101 generates target image data including target image P1 by photographing the person and their identification document. Terminal device 101 transmits the generated target image data to server device 102.

An identification document is an example of a document used to verify a person's identity. Examples of identification documents include a driver's license and a My Number card. A My Number card is a card that lists each resident's unique My Number.

Identification documents include the person's image, text, etc.

An image of the person is an image of the person, for example, an image of the person's face.

Characters include not only hiragana, katakana, kanji, and alphabets, but also numbers, symbols, etc. Characters written on an identity verification document represent the individual's name, address, date of birth, ID (Identifier), expiration date of the identity verification document, etc. Here, the ID on an identity verification document is, for example, a number unique to the individual shown on the identity verification document.

Figure 2 shows an example of a target image P1. The target image P1 is an image to be processed for identity verification. The target image P1 includes the individual and an identification document. Target image data including the target image P1 is typically obtained by simultaneously photographing the individual and the identification document, such as by photographing the individual holding the identification document in their hand, as shown in Figure 2.

Note that the target image P1 need only include an image of the individual and an image of the identification document, and the method of capturing the target image data including the target image P1 is not limited to this. For example, the identification document may be placed on a desk in front of the individual, or may be photographed together with the individual.

As shown in the figure, the target image P1 includes a first area AR1, a second area AR2, and a third area AR3.

The first area AR1 is an area in the target image P1 that shows the person (e.g., the person's face).

The second area AR2 and the third area AR3 are included in the area of the target image P1 that shows the identification document. The second area AR2 is the area of the target image P1 that shows the image of the person (e.g., the person's face image) included in the identification document. The third area AR3 is the area of the target image P1 that shows the characters included in the identification document.

(Functional configuration of server device 102)
The server device 102 is a device that manages the trail of identity verification, and as shown in FIG. 1, includes a data acquisition unit 103 and a trail generation unit 104.

The data acquisition unit 103 acquires the target image data from the terminal device 101 via the network N.

The trail generation unit 104 generates trail data based on the target image P1 when the first area AR1 and the second area AR2 satisfy the first condition and the clarity of the target image P1 satisfies the second condition.

(First condition)
The first condition is a condition for determining that the images in the first area AR1 and the second area AR2 are of the same person.

For example, the first condition is defined by the relationship between the degree of coincidence between the first area AR1 and the second area AR2 and a first reference value. The degree of coincidence is a value indicating the degree to which the first area AR1 and the second area AR2 coincide.

If the degree of match between the first area AR1 and the second area AR2 is greater, the degree of match will be greater. If the degree of match between the first area AR1 and the second area AR2 is greater, the degree of match will be smaller. If the degree of match between the first area AR1 and the second area AR2 is greater, the degree of match will be less than the first reference value.

Such a degree of similarity can be determined using conventional methods, for example, histogram comparison or feature point matching.

Also, for example, whether the first area AR1 and the second area AR2 satisfy the first condition may be determined using a trained model trained by machine learning. In this case, the trail generation unit 104 inputs the target image data into the person determination model, and outputs a determination result as to whether the images in the first area AR1 and the second area AR2 represent the same person. This person determination model is a trained machine learning model that has been trained to determine whether the images in the first area AR1 and the second area AR2 represent the same person.

The judgment result may be indicated by an appropriate value, alphabet, symbol, etc. For example, if the images in the first area AR1 and the second area AR2 are of the same person, the judgment result is "1", and if the images in the first area AR1 and the second area AR2 are not of the same person, the judgment result is "0".

The input data to the person determination model during learning is data containing two human images (preferably two facial images). In machine learning, it is advisable to perform supervised learning using training data that contains correct determination results regarding whether the human images contained in the input data are the same person.

(Second condition)
The second condition is a condition for adopting the target image P1 as evidence. For example, the second condition is defined by the relationship between the sharpness of the target image P1 and a second reference value.

Sharpness is a value that indicates the degree of sharpness of the target image P1. If the sharper the target image P1, the greater the sharpness value, the second condition is, for example, that the sharpness is equal to or greater than a second reference value. If the sharper the target image P1, the smaller the sharpness value, the second condition is, for example, that the sharpness is equal to or less than a second reference value.

Sharpness can be determined using conventional methods.

For example, the sharpness is determined by image processing such as edge extraction (also called "edge detection"), which involves calculating changes in brightness. In this case, the trail generation unit 104 determines the sharpness of the target image P1 using image processing.

Also, for example, the sharpness may be determined using a learning model trained by machine learning. In this case, the evidence generation unit 104 determines the sharpness of the target image P1 by inputting the target image data into a sharpness determination model trained by machine learning for determining the sharpness of the target image P1. The input data to the sharpness determination model during learning is, for example, the target image data. In machine learning, it is preferable to perform supervised learning using training data including the sharpness of the target image P1.

(evidence data)
The evidence data is data indicating the evidence and includes an image based on the target image P1. The image based on the target image P1 may be the target image P1 itself, as shown in Figure 2, or an image that has been processed on the target image P1. The processing on the target image P1 is preferably processing that leaves part or all of the first area AR1, second area AR2, and third area AR3 intact, such as trimming to remove peripheral areas.

(Physical Configuration of the Trail Management System 100)
The trail management system 100 is physically composed of a terminal device 101 and a server device 102 connected via a network N. The terminal device 101 and the server device 102 are each composed of a single, physically separate device.

The terminal device 101 and the server device 102 may be physically configured as a single device, in which case the terminal device 101 and the server device 102 are connected by an internal bus 1010 (described below) instead of the network N. Also, one or both of the terminal device 101 and the server device 102 may be physically configured as multiple devices connected via the network N.

(Physical configuration of terminal device 101)
The terminal device 101 is physically, for example, a tablet PC (Personal Computer), a smartphone, a general-purpose computer, or the like.

In detail, for example, the terminal device 101 physically includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, a network interface 1050, an input interface 1060, an output interface 1070, and a camera 1080, as shown in FIG. 3.

The bus 1010 is a data transmission path through which the processor 1020, memory 1030, storage device 1040, network interface 1050, input interface 1060, output interface 1070, and camera 1080 transmit and receive data to and from each other. However, the method of connecting the processor 1020 and other components to each other is not limited to bus connection.

Processor 1020 is a processor realized by a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).

Memory 1030 is a main storage device realized by RAM (Random Access Memory) or the like.

Storage device 1040 is an auxiliary storage device realized by an HDD (Hard Disk Drive), SSD (Solid State Drive), memory card, or ROM (Read Only Memory), etc.

The storage device 1040 of the terminal device 101 stores program modules for realizing the functions of the terminal device 101. The processor 1020 loads each of these program modules into the memory 1030 and executes them, thereby realizing the function corresponding to that program module.

The network interface 1050 is an interface for connecting the terminal device 101 to the network N.

The input interface 1060 is an interface for the user to input information, and is composed of one or more of, for example, a touch panel, a keyboard, a mouse, etc.

The output interface 1070 is an interface for presenting information to the user, such as an LCD panel or an organic EL (Electro-Luminescence) panel.

Camera 1080 captures an image of a subject and generates image information representing the image.

(Physical configuration of server device 102)
The server device 102 is physically, for example, a tablet PC (Personal Computer), a smartphone, a general-purpose computer, or the like.

In detail, for example, the server device 102 may be physically configured in the same manner as the terminal device 101, as shown in Figure 4. That is, the server device 102 may physically include, for example, a bus 1010, a processor 1020, a memory 1030, a storage device 1040, a network interface 1050, an input interface 1060, and an output interface 1070.

The storage device 1040 of the server device 102 stores program modules for realizing the functions of the server device 102. The processor 1020 loads each of these program modules into the memory 1030 and executes them, thereby realizing the function corresponding to that program module.

(Operation of the trail management system 100)
The operation of the trail management system 100 will now be described with reference to the drawings.

The evidence management system 100 executes an evidence management process. The evidence management process is a process for managing the evidence of identity verification, and includes a first terminal process executed by the terminal device 101 and a first server process executed by the server device 102.

(First terminal processing)
5 is a flowchart showing an example of the first terminal processing according to the present embodiment. The first terminal processing is started, for example, when the terminal device 101 receives a start instruction based on an input from the user or a start instruction from a functional unit (not shown) that executes processing prior to the first terminal processing. At this time, in response to receiving the start instruction from the user, the terminal device 101 establishes communication with the server device 102 via the network N.

The terminal device 101 generates target image data including the target image P1 by photographing the person and their identification document (step S101).

The terminal device 101 transmits the target image data generated in step S101 to the server device 102 (step S102) and terminates the first terminal processing.

(First Server Processing)
6 is a flowchart showing an example of the first server process according to the present embodiment. When communication is established between the server device 102 and the terminal device 101 via the network N, the server device 102 starts the first server process.

The data acquisition unit 103 acquires the target image data transmitted in step S102 from the terminal device 101 via the network N (step S201).

The trail generation unit 104 determines whether the first and second conditions are satisfied for the target image P1 contained in the target image data acquired in step S210 (step S202).

If at least one of the first condition and the second condition is not met, the trail generation unit 104 determines that the first condition and the second condition are not met. If it determines that the first condition and the second condition are not met (step S202; No), the trail generation unit 104 notifies the terminal device 101 of an error via the network N (step S203) and terminates the first server processing.

The error notification sent in step S203 is a notification indicating that at least one of the first condition and the second condition is not met, and is acquired by the terminal device 101. In response to acquiring the error notification, the terminal device 101 displays a predetermined message on the display unit, such as "An appropriate image was not captured" or "Identity verification failed."

If it is determined that the first and second conditions are met (step S202; Yes), the trail generation unit 104 generates trail data (step S204) and terminates the first server processing. Because the trail data is generated based on the target image P1 whose clarity satisfies the second condition, it is possible to generate trail data that includes a clear image.

According to this embodiment, the server device 102 as an evidence management device comprises a data acquisition unit 103 and an evidence generation unit 104.

The data acquisition unit 103 acquires target image data including a target image P1. The target image P1 includes a first area AR1 representing the individual and a second area AR2 representing the individual's image included in an identification document.

The trail generation unit 104 generates trail data based on the target image P1 when the first area AR1 and the second area AR2 satisfy the first condition and the clarity of the target image P1 satisfies the second condition.

This makes it possible to maintain evidence of good image quality.

According to this embodiment, the evidence management system 100 comprises a server device 102 as an evidence management device and a terminal device 101.

The terminal device 101 generates target image data by photographing the person and the identification document as a document, and transmits the target image data to the server device 102.

This makes it possible to maintain evidence of good image quality.

(Variation 1)
The trail generating unit 104 may calculate the sharpness of the target image P1 based on the third area AR3.

When image processing such as edge extraction is used to determine sharpness, the evidence generation unit 104 may determine the sharpness of the target image P1 by performing image processing on the third area AR3.

A sharpness determination model trained by machine learning may be used to determine the sharpness. In this case, the evidence generation unit 104 may determine the sharpness of the target image P1 by inputting the target image data into a sharpness determination model trained by machine learning to determine the sharpness of the third region AR3. The input data to the sharpness determination model during learning is, for example, the target image data. In the machine learning, supervised learning may be performed using training data including the sharpness of the third region AR3.

In this modified example, the evidence generation unit 104 calculates the sharpness of the target image P1 based on the third area AR3. Generally, images of text have greater changes in brightness relative to the background than images such as photographs.

Therefore, by calculating the sharpness of the target image P1 based on the third area AR3, which indicates the characters contained in the identification document, the sharpness of the target image P1 can be appropriately calculated. Furthermore, evidence data containing clear images can be generated more reliably. Therefore, this modification makes it possible to manage evidence with better image quality.

<<Embodiment 2>>
In the second embodiment, a more detailed example of the trail management system 100 according to the first embodiment will be described. In this embodiment, differences from the first embodiment will be mainly described, and overlapping points will be omitted as appropriate for the sake of simplicity.

As shown in FIG. 7, the evidence management system 200 of embodiment 2 of the present invention includes a terminal device 201 and a server device 202, which replace the terminal device 101 and server device 102 of embodiment 1.

(Functional configuration of terminal device 201)
Functionally, the terminal device 201 is similar to the first embodiment in that it takes an image of the person and their identification document to generate target image data and transmits the target image data to the server device 202 .

In detail, the terminal device 201 comprises an imaging unit 205, an input receiving unit 206, a terminal communication unit 207, and a display unit 208.

The photographing unit 205 photographs the person and their identification document. As a result, the photographing unit 205 generates target image data including the target image P1.

The input acceptance unit 206 accepts input from the person. The terminal communication unit 207 sends and receives data to and from the server device 102 via the network N. The display unit 208 displays various information such as images and text.

(Functional configuration of server device 202)
The server device 202 is a device (trail management device) for managing the trail of identity verification, similar to the first embodiment, and includes the same data acquisition unit 103 as in the first embodiment, and a trail generation unit 204 that replaces the trail generation unit 104 according to the first embodiment. The server device 202 further includes a trail data storage unit 209 and a to-be-verified data storage unit 210.

As in embodiment 1, the trail generation unit 204 generates trail data based on the target image P1 when the first area AR1 and the second area AR2 satisfy the first condition and the clarity of the target image P1 satisfies the second condition.

In detail, the trail generation unit 204 includes a first determination unit 211, a second determination unit 212, a memory control unit 213, and a notification unit 214, as shown in FIG. 8.

The first judgment unit 211 judges whether the first area AR1 and the second area AR2 of the target image P1 contained in the target image data acquired by the data acquisition unit 103 satisfy the first condition.

When the first judgment unit 211 judges that the first condition is satisfied, the second judgment unit 212 judges whether the clarity of the target image P1 judged to satisfy the first condition satisfies the second condition.

The storage control unit 213 generates trail data or data requiring verification depending on whether it is determined that the second condition is met. If the storage control unit 213 generates trail data, it stores the trail data in the trail data storage unit 209, and if it generates data requiring verification, it stores the data requiring verification in the data requiring verification storage unit 210.

The evidence data is data indicating evidence, as described in embodiment 1. The confirmation-required data is data indicating the target image P1 that requires visual confirmation.

The evidence data and the data to be confirmed have in common the fact that they both contain an image based on the target image P1 that was the subject of judgment by the second judgment unit 212. As explained in embodiment 1, the image based on the target image P1 may be the target image P1 itself, or an image that has been subjected to processing such as cropping of the target image P1.

The trail data and the data requiring verification are stored in different storage units 209, 210. That is, the trail data and the data requiring verification are stored in storage areas of different classifications in the storage device 1040, such as different folders.

The trail data and the data to be verified may include information to distinguish them from each other. In this case, the trail data and the data to be verified may be stored in a common storage unit (i.e., a storage area in storage device 1040 that belongs to a common classification).

The notification unit 214 sends a notification to the terminal device 201 via the network N.

Referring again to FIG.
The trail data storage unit 209 is a storage unit in which trail data is stored. The verification data storage unit 210 is a storage unit in which verification data is stored.

(Physical configuration of the trail management system 200, terminal device 201, and server device 202)
The trail management system 200, terminal device 201, and server device 202 may be physically configured in the same manner as the trail management system 100, terminal device 101, and server device 102 according to the first embodiment.

(Operation of the trail management system 200)
The operation of the trail management system 200 will now be described with reference to the drawings.

The evidence management system 200 executes an evidence management process for managing evidence of identity verification, similar to embodiment 1. The evidence management process according to this embodiment includes a second terminal process and a second server process that replace the first terminal process and first server process according to embodiment 1. The second terminal process is executed by the terminal device 201. The second server process is executed by the server device 202.

(Second terminal processing)
9 is a flowchart showing an example of the second terminal processing according to the present embodiment. The second terminal processing is started, for example, when a start instruction similar to that of the first terminal processing is received. At this time, in response to receiving the start instruction from the user, the terminal device 201 establishes communication with the server device 202 via the network N.

The photographing unit 205 displays the preview image PP together with the guides G1 and G2 on the display unit 208 (step S103).

The preview image PP is an image used to check the image of the person to be photographed before the target image P1 is photographed by the photographing unit 205. Figure 10 shows an example of a preview image PP. As shown in the figure, the preview image PP is displayed together with guides G1 and G2.

Guide G1 is a guide that indicates the range of the facial image that should be positioned in the preview image PP in order to capture the person's facial image at the desired size. Guide G2 is a guide that indicates the range of the image of the identification document that should be positioned in the preview image PP in order to capture the identification document at the desired size.

The photographing unit 205 determines whether a target image P1 including an image of the person's face and identification document of a predetermined size is photographed (step S104).

If at least one of the person's face and the identification document is not of a predetermined size, the imaging unit 205 determines that a target image P1 including an image of the person's face and the identification document of a predetermined size has not been captured. If it determines that a target image P1 including an image of the person's face and the identification document of a predetermined size has not been captured (step S104; No), the imaging unit 205 continues to execute the processing of step S103.

While referring to the preview image PP, the person adjusts the position of their face relative to the image capturing unit 205 (camera 1080) so that their facial image roughly matches guide G1. Similarly, for the personal identification document, the person adjusts the position of the personal identification document relative to the image capturing unit 205 (camera 1080) so that the image on the personal identification document roughly matches guide G2.

When the facial image and the image of the identification document in the preview image PP roughly match the guides G1 and G2, respectively, the capturing unit 205 determines that a target image P1 containing an image of the person's face and identification document of a predetermined size has been captured.

If it is determined that the person's face and identification document are to be photographed at a predetermined size (step S104; Yes), the photographing unit 205 executes step S101, the same as in embodiment 1. This generates target image data including an image of the person's face and identification document at a predetermined size.

The terminal communication unit 207 executes step S102 similar to embodiment 1 and terminates terminal processing.

(Second Server Processing)
11 is a flowchart showing an example of the second server process according to the present embodiment. As in the first embodiment, the server device 202 starts the second server process when communication with the terminal device 201 via the network N is established.

The data acquisition unit 103 executes step S201 similar to that of embodiment 1.

The first judgment unit 211 determines whether the first area AR1 and the second area AR2 of the target image P1 included in the target image data acquired in step S201 satisfy the first condition (step S205).

If it is determined that the first condition is met (step S205; Yes), the second judgment unit 212 determines whether the clarity of the target image P1 that was determined to meet the first condition in step S205 meets the second condition (step S206).

If it is determined that the second condition is met (step S206; Yes), the storage control unit 213 executes step S204, which is the same as in embodiment 1. The storage control unit 213 stores the trail data generated in step S204 in the trail data storage unit 209 (step S207).

The notification unit 214 notifies the terminal device 101 via the network N of the successful identity verification (step S208) and terminates the second server processing.

The success notification sent in step S208 is a notification indicating that the first condition and the second condition have been met, and is acquired by the device communication unit 207. In response to acquiring the success notification, the device communication unit 207 causes the display unit 208 to display a predetermined message such as, for example, "Identity verification has been successful."

If it is determined that the second condition is not met (step S206; No), the storage control unit 213 generates confirmation-required data (step S209). The storage control unit 213 stores the confirmation-required data generated in step S207 in the confirmation-required data storage unit 210 (step S210). As in embodiment 1, the notification unit 214 notifies the terminal device 201 of an error via the network N (step S203), and terminates the second server processing.

The error notification sent in step S203 is a notification indicating that the second condition is not met, and is acquired by the device communication unit 207. In response to acquiring the error notification, the device communication unit 207 causes the display unit 208 to display a predetermined message, such as "An appropriate image was not captured" or "Identity verification failed."

If it is determined that the first condition is not met (step S205; No), the notification unit 214 notifies the terminal device 201 of an error via the network N (step S211) and terminates the second server processing.

The error notification sent in step S211 is a notification indicating that the first condition is not satisfied, and is acquired by the device communication unit 207. In response to acquiring the error notification, the device communication unit 207 causes a predetermined message to be displayed on the display unit 208. The message displayed in response to the error notification sent in step S211 may be the same as or different from the message displayed in response to the error notification sent in step S203.

(Variation 2)
In the first embodiment, an example has been described in which the evidence data includes an image based on the target image P1. However, the evidence data may include information other than an image based on the target image P1.

Figure 12 is a diagram showing an example of the configuration of evidence data related to variant example 2. The evidence data shown in the figure is data in which an image based on target image P1, an evidence ID, a time, and text information are associated with each other.

The trail ID is information for identifying the associated image and is assigned appropriately, for example, when generating the trail data. The time is the time when the trail data containing the associated image was generated. Note that if the trail data is stored in a memory unit, the time may also be the time when the trail data was stored in the memory unit.

The character information is information indicating characters recognized from the image of the third area AR3, and is generated by the evidence generation unit 104. In the evidence data generation process (step S204), the evidence generation unit 104 (storage control unit 213) recognizes the characters shown in the third area AR3, generates character information including the recognized characters, and generates evidence data including the generated character information.

According to this modification, the evidence data includes text information based on the image of the third area AR3, i.e., information indicating characters recognized from the image of the third area AR3. This allows users of the server devices 102 and 202 to easily refer to the characters written on the identification document without viewing the evidence data. This makes it easier to manage the evidence.

In addition, if necessary, the user of the server device 102, 202 can compare the text information shown in the image of the identification document included in the evidence data with the text information included in the evidence data.

This makes it easy to determine whether the image of the identification document included in the evidence data is of such good quality that it can be accurately read by character recognition. For example, if there is a misrecognition of character information, measures can be taken, such as improving the process for determining whether the clarity of the target image P1 satisfies the second condition, to obtain evidence data of better quality. This makes it possible to manage evidence of good quality.

<<Embodiment 3>>
When the second condition is defined by the relationship between the sharpness of the target image P1 and the second reference value, the second reference value applied to the second condition is not limited to one, but may be appropriately selected from multiple candidate values. In this embodiment, an example will be described in which a candidate value selected from multiple candidate values in descending order of magnitude is adopted as the second reference value. In this embodiment, differences from the second embodiment will be mainly described, and overlapping points will be omitted as appropriate for simplicity.

As shown in Figure 13, the evidence management system 300 of embodiment 3 of the present invention includes a terminal device 201 similar to that of embodiment 2 and a server device 302 that replaces the server device 202 of embodiment 2.

(Functional configuration of server device 302)
The server device 302 is a device (trail management device) for managing the trail of identity verification, similar to embodiment 2. The server device 302 includes the data acquisition unit 103 similar to embodiment 1, the trail data storage unit 209 and the verification-required data storage unit 210 similar to embodiment 2, and a trail generation unit 304 that replaces the trail generation unit 204 according to embodiment 2.

As in embodiment 1, the trail generation unit 304 generates trail data based on the target image P1 when the first area AR1 and the second area AR2 satisfy the first condition and the clarity of the target image P1 satisfies the second condition.

In detail, as shown in Figure 14, the evidence generation unit 304 includes a first determination unit 211, a memory control unit 213, and a notification unit 214 similar to those in embodiment 2, and a second determination unit 312 that replaces the second determination unit 212 in embodiment 2. The evidence generation unit 304 further includes a candidate storage unit 315.

The candidate storage unit 315 stores candidate data 316. The candidate data 316 includes multiple candidate values. The candidate values are candidates for the second reference value to be applied to the second condition. In this embodiment, the candidate values are set in advance by user input, etc.

The candidate data 316 according to this embodiment includes three candidate values, C1, C2, and C3, as shown in an example in Figure 15. C1, C2, and C3 have a relationship of C1 > C2 > C3 in terms of the magnitude of each value.

Note that the number of candidate values included in candidate data 316 is not limited to three, and may be any number.

The second determination unit 312 in this embodiment repeatedly performs the second determination process by using the candidate value selected from the plurality of candidate values in descending order as the second reference value. The "second determination process" is a process for determining whether the sharpness of the target image P1 satisfies the second condition, and the same applies hereinafter.

Furthermore, the second determination unit 312 according to this embodiment repeatedly executes the second determination process until the following (A) or (B).
(A) Using one of the candidate values as a second reference value, it is determined that the sharpness of the target image P1 satisfies the second condition. (B) The second determination process is performed for all candidate values.

Except for these, the second judgment unit 312 may be similar to the second judgment unit 212 in embodiment 2.

(Physical configuration of the trail management system 300 and the server device 302)
The trail management system 300 and the server device 302 may be physically configured in the same manner as the trail management system 100 and the server device 102 according to the first embodiment.

(Operation of the trail management system 300)
The operation of the trail management system 300 will now be described with reference to the drawings.

The trail management system 300 executes a trail management process for managing the trail of identity verification, similar to embodiment 1. The trail management process according to this embodiment includes a second terminal process similar to embodiment 2, and a third server process that replaces the second server process according to embodiment 2. The third server process is executed by the server device 302.

(Third Server Processing)
16 is a flowchart showing an example of the third server process according to the present embodiment. When communication via network N is established with terminal device 201, server device 302 starts the third server process. In the third server process, second determination unit 312 sets counter i, which is updated according to the number of times the second determination process is repeated, to "1" as an initial setting.

Steps S201 and S205 are executed in the same manner as in embodiment 2.

If it is determined that the first condition is not met (step S205; No), the notification unit 214 executes step S211 similar to embodiment 2 and terminates the third server processing.

If it is determined that the first condition is met (step S205; Yes), the second determination unit 312 selects Ci from the candidate values included in the candidate data 316 as the second reference value (step S212).

For example, when step S212 is executed for the first time, i has an initial value of "1", so the second judgment unit 312 selects C1 as the second reference value.

The second judgment unit 312 executes step S206, similar to embodiment 2, using the second reference value selected in step S212.

If it is determined that the second condition is met (step S206; Yes), the memory control unit 213 executes steps S204 and S207 to S208 similar to those in embodiment 2, and terminates the third server processing.

If it is determined that the second condition is not satisfied (step S206; No), the second judgment unit 312 determines whether the second judgment process (step S206) has been performed for all candidate values (step S213).

In this embodiment, as described above, there are three candidate values. Therefore, until the second determination process (step S206) has been performed for all three candidate values, the second determination unit 312 determines that the second determination process (step S206) has not been performed for all candidate values.

If it is determined that the second judgment process (step S206) has not been performed for all candidate values (step S213; No), the second judgment unit 312 adds 1 to i (step S214).

The second judgment unit 312 selects the candidate value Ci corresponding to the i added in step S214 as the second reference value (step S212).

For example, when i is 2, the second judgment unit 312 selects C2 as the second reference value. For example, when i is 3, the second judgment unit 312 selects C3 as the second reference value.

The second judgment unit 312 again executes step S206, similar to embodiment 2, using the second reference value selected in step S212.

If it is determined that the second condition is met (step S206; Yes), the memory control unit 213 executes steps S204 and S207 to S208 similar to those in embodiment 2, and terminates the third server processing.

If it is determined that the second condition is not satisfied (step S206; No), the second determination unit 312 executes step S213. If it is determined that the second determination process (step S206) has not been executed for all candidate values (step S213; No), the second determination unit 312 adds 1 to i (step S214).

In this way, if it is determined that the second condition is not met (step S206; No), steps S213-S214, S212, and S206 are repeated until the second determination process (step S206) is performed for all candidate values C1-C3. On the other hand, if it is determined that the second condition is met for any of candidate values C1-C3 (step S206; Yes), steps S204 and S207-S208 are performed, and the third server process ends.

If it is determined that the second determination process has been performed for all candidate values (step S213; Yes), the memory control unit 213 executes steps S209 to S210 and S203 and terminates the third server process.

According to this embodiment, the second condition is defined by the relationship between the sharpness of the target image P1 and the second reference value. The evidence generation unit 304 holds candidate data 316 including multiple candidate values that are candidates for the second reference value. The evidence generation unit 304 repeatedly determines whether the sharpness of the target image P1 satisfies the second condition by using, as the second reference value, a candidate value selected from the multiple candidate values in descending order.

As a result, the clearer the target image P1, the larger the second reference value used in the second determination process to generate evidence data. Therefore, the second reference value used in the second determination process immediately before generating the evidence data serves as an index representing the clarity of the target image P1. For example, by including the second reference value used in the second determination process immediately before generating the evidence data in the evidence data, the clarity of the target image P1 can be easily determined by making the second reference value available for reference. This makes it possible to improve the convenience of managing evidence.

(Variation 3)
For example, the terminal device 101, 201 can continuously generate and transmit target image data to the server device 102, 202 by repeatedly executing the first or second terminal process at a predetermined time interval (for example, once to several tens of times per second).

The server device 102, 202 may repeatedly execute the first or second server process for each of the target image data acquired sequentially until a predetermined time has elapsed or until the predetermined maximum number of executions of the second determination process has been reached.

Figure 17 is a flowchart showing an example of a fourth server process according to this modified example. The fourth server process is a server process in which the second server process according to embodiment 2 is repeatedly executed until a predetermined maximum number of executions M of the second determination process is reached.

As shown in the figure, if it is determined that the second condition is not satisfied (step S206; No), the second determination unit 312 determines whether the second determination process has been executed M times (step S215), where M is a predetermined maximum number of times.

If it is determined that the second determination process has not been executed M times (step S215; No), the data acquisition unit 103 executes step S201 again. If it is determined that the second determination process has been executed M times (step S215; Yes), the storage control unit 213 executes steps S209 to S210 and S203, and then terminates the fourth server process. Except for these, the fourth server process may be the same as the second server process.

In this modified example, there are multiple pieces of target image data. Therefore, it is possible to determine whether the first and second conditions are met for multiple pieces of target image data. This makes it easier to obtain a target image P1 that satisfies the second condition than when determining whether the first and second conditions are met for a single piece of target image data. Therefore, it becomes possible to manage evidence of good image quality.

<<Fourth Embodiment>>
In this embodiment, an example will be described in which the third server process according to the third embodiment is repeatedly executed for each of a plurality of target image data until the predetermined maximum number of executions of the second determination process is reached, as in Modification Example 3. In this embodiment, differences from the third embodiment will be mainly described, and overlapping points will be omitted as appropriate for the sake of simplicity.

As shown in Figure 18, the evidence management system 400 of embodiment 4 of the present invention comprises a terminal device 201 similar to that of embodiment 2 and a server device 402 replacing the server device 302 of embodiment 3.

(Functional configuration of server device 402)
The server device 402 is a device (trail management device) for managing the trail of identity verification, similar to embodiment 3. The server device 402 includes the data acquisition unit 103 similar to embodiment 1, the trail data storage unit 209 and the verification-required data storage unit 210 similar to embodiment 2, and a trail generation unit 404 that replaces the trail generation unit 304 according to embodiment 3.

As in embodiment 1, the trail generation unit 404 generates trail data based on the target image P1 when the first area AR1 and the second area AR2 satisfy the first condition and the clarity of the target image P1 satisfies the second condition.

In detail, as shown in FIG. 19, the evidence generation unit 404 includes a first determination unit 211, a memory control unit 213, and a notification unit 214 similar to those in embodiment 2, and a second determination unit 412 and a candidate storage unit 415 that replace the second determination unit 312 and the candidate storage unit 315 in embodiment 3.

The candidate storage unit 415 stores candidate data 416 containing multiple candidate values similar to those in embodiment 3.

The candidate data 416 according to this embodiment is data in which the maximum number of executions M1, M2, and M3 of the second determination process are associated with three candidate values C1, C2, and C3, respectively, as shown in an example in Figure 20. In the candidate data 416, the maximum number of executions M1 is associated with candidate value C1, the maximum number of executions M2 is associated with candidate value C2, and the maximum number of executions M2 is associated with candidate value C2.

As in embodiment 3, C1, C2, and C3 have a relationship of C1>C2>C3 in terms of the magnitude of each value. M1, M2, and M3 have a relationship of M1<M2<M3 in terms of the magnitude of each value. That is, in candidate data 416, the maximum number of executions is larger as the candidate value is smaller.

Here, setting the maximum number of times the second determination process is executed is equivalent to setting a time limit for repeatedly executing the fourth server process. In other words, the larger the maximum number of times it is executed, the longer the time limit.

Note that the number of candidate values included in candidate data 416 is not limited to three, and can be any number.

As in embodiment 2, when the first judgment unit 211 judges that the first condition is satisfied, the second judgment unit 412 performs a second judgment process on the target image P1 that is judged to satisfy the first condition.

The second determination unit 412 according to this embodiment repeatedly executes the second determination process up to (A), (B) as in the third embodiment, or (C) below.
(C) The second determination process is repeated the maximum number of times associated with the candidate value.

Except for these, the second judgment unit 412 may be similar to the second judgment unit 312 in embodiment 3.

(Physical configuration of the trail management system 400 and the server device 402)
The trail management system 400 and the server device 402 may be physically configured in the same manner as the trail management system 100 and the server device 102 according to the first embodiment.

(Operation of the trail management system 400)
The operation of the trail management system 400 will now be described with reference to the drawings.

The trail management system 400 executes a trail management process for managing the trail of identity verification, similar to embodiment 1. The trail management process according to this embodiment includes a second terminal process similar to embodiment 2, and a fifth server process that replaces the third server process according to embodiment 3. The fifth server process is executed by the server device 402.

(Fifth Server Process)
21 is a flowchart showing an example of the fifth server process according to the present embodiment. As in the first embodiment, the server device 402 starts the fifth server process when communication is established with the terminal device 201 via the network N. In the fifth server process, the second determination unit 412 sets, as an initial setting, "1" to a counter i, which is updated depending on the number of times the second determination process is repeated.

Steps S201 and S205 are executed in the same manner as in embodiment 2.

If it is determined that the first condition is not met (step S205; No), the notification unit 214 executes step S211 similar to embodiment 2 and terminates the fifth server processing.

If it is determined that the first condition is met (step S205; Yes), the second determination unit 312 executes step S212 similar to that of embodiment 3, and executes step S206 similar to that of embodiment 2 using the second reference value selected in step S212.

If it is determined that the second condition is met (step S206; Yes), the memory control unit 213 executes steps S204 and S207 to S208 similar to those in embodiment 2, and terminates the fifth server processing.

If it is determined that the second condition is not met (step S206; No), the second determination unit 312 executes step S215, which is the same as in variant example 3.

If it is determined that the second determination process has not been executed M times (step S215; No), the data acquisition unit 103 executes step S201 again. If it is determined that the second determination process has been executed M times (step S215; Yes), the second determination unit 312 executes step S213, which is the same as in embodiment 3.

If it is determined that the second determination process (step S206) has not been performed for all candidate values (step S213; No), the second determination unit 312 performs step S214 and then step S212. If it is determined that the second determination process has been performed for all candidate values (step S213; Yes), the storage control unit 213 performs steps S209 to S210 and S203, and then ends the fifth server process.

In this embodiment, the target image P1 to be included in the data to be confirmed in step S209 may be, for example, the target image P1 included in the target image data most recently acquired by the data acquisition unit 103.

Also, for example, the target image P1 to be included in the data to be confirmed in step S209 may be the target image P1 with the highest clarity among the target images P1 included in the multiple target image data acquired by the data acquisition unit 103. This allows the target image P1 with the highest clarity to be included in the data to be confirmed, making visual confirmation easier.

In this embodiment, there are multiple target image data. Candidate data 416 is data in which the maximum number of executions of the process (second determination process) for determining whether the sharpness of target image P1 satisfies the second condition is associated with each of multiple candidate values. In candidate data 416, the maximum number of executions is larger the smaller the candidate value. This makes it possible to generate evidence data based on target image P1 that is as clear as possible. Therefore, it becomes possible to manage evidence with good image quality.

(Variation 4)
In the fourth embodiment, as an example of changing the maximum number of executions depending on the second reference value, an example has been described in which a larger maximum number of executions is associated with a smaller candidate value. However, a smaller maximum number of executions may also be associated with a smaller candidate value.

Generally, the smaller the second reference value, the easier it may be to obtain a target image P1 with a clarity that satisfies the second condition. In such cases, the smaller the candidate value, the smaller the maximum number of executions, thereby shortening the overall time required to obtain evidence data. Therefore, it becomes easier to obtain evidence of good image quality.

(Variation 5)
In the fourth embodiment and the fourth modification, examples have been described in which the maximum number of executions varies depending on the second reference value. However, when different values of the second reference value are used, a common maximum number of executions may be applied to all of the second reference values. This corresponds to setting M1, M2, and M3 to the same value in the fourth embodiment.

This also allows the second judgment process to be performed using gradually smaller second reference values, making it possible to generate evidence data based on the target image P1 that is as clear as possible. Therefore, it becomes possible to manage evidence with good image quality.

(Variation 6)
In the fourth embodiment, an example has been described in which sequentially acquired target images P1 are used in the second determination process (the process from step S205 onward) in which each candidate value is used as the second reference value. In the fourth embodiment, a group of target images that do not overlap with each other is used in the second determination process (the process from step S205 onward) in which each candidate value is used as the second reference value.

Specifically, for example, the second judgment process using C1 as the second reference value, the second judgment process using C2 as the second reference value, and the second judgment process using C3 as the second reference value will use groups of target images that do not overlap with each other.

However, for example, the data acquisition unit 103 may hold target image data acquired from the terminal device 201. In the fourth embodiment, in the second determination process (processing from step S205 onwards) in which a certain candidate value is used as the second reference value, some or all of the target image group used in the second determination process (processing from step S205 onwards) in which an earlier candidate value was used as the second reference value may be used.

Specifically, for example, in the second determination process in which C2 is used as the second reference value, some or all of the target images used in the second determination process in which C1, the previous candidate value, was used as the second reference value may be used. Also, for example, in the second determination process in which C3 is used as the second reference value, some or all of the target images used in the second determination process in which C2, the previous candidate value, was used as the second reference value may be used.

According to this modified example, in the second determination process (processing from step S205 onwards), a common target image P1 is used for the second determination process (processing from step S205 onwards) in which a different candidate value is used as the second reference value. Therefore, the time required to acquire target image data from the terminal device 201 can be shortened throughout the repeated second determination process (processing from step S205 onwards). This makes it possible to improve the time efficiency for managing evidence of good image quality.

<<Embodiment 5>>
In the third and fourth embodiments, examples were described in which candidate values for the second reference value were set in advance. However, even if the image capturing unit 205 of the terminal device 201 can capture a clear image, if the second determination process is performed using a relatively small second reference value, evidence data based on a target image P1 that is less clear than would otherwise be obtained may be generated. Therefore, it is desirable to determine the clarity of the target image P1 using a second reference value that corresponds to the performance of the camera of the terminal device 201.

In this embodiment, we will explain an example in which embodiment 3 is modified so that candidate values are set according to the performance of the camera equipped in the terminal device 201.

As shown in FIG. 22, the evidence management system 500 of embodiment 5 of the present invention comprises a terminal device 201 similar to that of embodiment 2 and a server device 502 replacing the server device 302 of embodiment 3.

(Functional configuration of server device 502)
The server device 502 is a device (trail management device) for managing the trail of identity verification, similar to the third embodiment. The server device 502 includes a preparation unit 517 in addition to the functional components 103, 304, 209, and 210 of the server device 302 according to the third embodiment.

The preparation unit 517 acquires multiple reference data and determines multiple candidate values based on the clarity of each of the images contained in the acquired multiple reference data.

Reference data is data that includes a reference image. In this embodiment, we will explain an example in which each piece of reference data includes the target image P1 as a reference image. Note that the reference image only needs to include at least a character image.

In detail, the preparation unit 517 acquires multiple pieces of reference data and determines the sharpness of each of the images included in the acquired multiple pieces of reference data. The preparation unit 517 then determines a reference value based on the sharpness of the images included in the reference data. The reference value is, for example, the average value of the sharpness of the images included in the reference data. The preparation unit 517 determines each of the multiple candidate values based on the reference value.

(Physical configuration of the trail management system 500 and the server device 502)
The trail management system 500 and the server device 502 may be physically configured in the same manner as the trail management system 100 and the server device 102 according to the first embodiment.

(Operation of the trail management system 500)
The operation of the trail management system 500 will now be described with reference to the drawings.

The trail management system 500 executes a trail management process for managing the trail of identity verification, similar to embodiment 1. The trail management process according to this embodiment includes a second terminal process similar to embodiment 2, and a sixth server process that replaces the third server process according to embodiment 3. The sixth server process is executed by the server device 502.

(Sixth Server Process)
23 is a flowchart showing an example of the sixth server process according to the present embodiment. As in the first embodiment, the server device 502 starts the sixth server process when communication with the terminal device 201 via the network N is established.

In the sixth server process, as shown in the figure, steps S216 to S220 are executed before step S201, which is executed first in the fifth server process. Except for these steps, the sixth server process may be the same as the fifth server process.

The preparation unit 517 acquires multiple reference data including the target image P1 from the terminal device 201 via the network N (step S216).

The preparation unit 517 calculates the sharpness of each target image P1 contained in the reference data obtained in step S216 (step S217).

In step S217, the preparation unit 517 may calculate the sharpness of each target image P1 included in the reference data using the same method as the method used to calculate the sharpness of the target image P1 in the second determination process (step S206).

The preparation unit 517 calculates a reference value based on the multiple sharpness values calculated in step S217 (step S218).

In step S218, the preparation unit 517, for example, calculates the average value of the multiple sharpness values calculated in step S217 and uses this average value as a reference value.

The reference value is not limited to the average value of multiple sharpness levels, but may be any value obtained based on the sharpness of the image included in the reference data. The reference value may be, for example, the maximum or minimum value of multiple sharpness levels, or a value obtained by applying appropriate statistical processing to multiple sharpness levels.

The preparation unit 517 determines multiple candidate values based on the reference value obtained in step S218 (step S219).

In step S219, the preparation unit 517 determines multiple candidate values using the reference value as the lower limit. For example, the preparation unit 517 sets the reference value to C3, which is the smallest candidate value among C1, C2, and C3.

The preparation unit 517 sets the other candidate values C1 and C2 in a predetermined manner. For example, if the values are to be increased by a predetermined value d, the preparation unit 517 sets C2 to a value obtained by adding d to the reference value, and sets C3 to a value obtained by adding d to C2.

The preparation unit 517 may determine at least some of the multiple candidate values based on the reference value obtained in step S218. When some of the multiple candidate values are determined based on the reference value, the remaining candidate values may be values based on user input, predetermined values, etc.

The preparation unit 517 generates candidate data 316 including the multiple candidate values determined in step S219, and stores the generated candidate data 316 in the candidate storage unit 315 (step S220). Subsequently, the same processing as in the third server processing from step S201 onward is executed.

In this embodiment, an example of determining a candidate value based on a reference value has been described, but if there is one second reference value, the reference value or a value based on the reference value may be applied to the second reference value.

According to this embodiment, the preparation unit 517 acquires multiple pieces of reference data that include at least images of text, and determines multiple candidate values based on the sharpness of each image included in the acquired multiple pieces of reference data. This allows the sharpness of the target image P1 to be determined using a second reference value that corresponds to the performance of the camera included in the terminal device 201. This makes it possible to manage evidence of better image quality.

(Variation 7)
In the embodiment and the modified example, the trail management device is the server device 102, 202, 302, 402, or 502.

However, the terminal devices 101 and 201 may also be provided with, as a trail management device, the data acquisition unit 103 and the trail generation unit 104, 204, 304, and 404 provided in the server devices 102, 202, 302, 402, and 502. Furthermore, the terminal devices 101 and 201 may also be provided with, as a trail management device, the preparation unit 517 provided in the server device 502.

In this case, for example, the terminal communication unit 207 may transmit the generated trail data to the server device 102, 202 via the network N. Also, for example, the terminal device 101, 201 may be provided with a trail data storage unit 209, and the trail data may be stored in the trail data storage unit 209. Furthermore, for example, the terminal device 101, 201 may be provided with a data-to-be-confirmed storage unit 210, and the data-to-be-confirmed may be stored in the data-to-be-confirmed storage unit 210.

For example, the candidate values of candidate data 316 may be set according to instructions from server device 302 based on user input.

This also achieves the same effects as each embodiment and each variant.

The above describes embodiments and variations of the present invention with reference to the drawings, but these are merely examples of the present invention and various configurations other than those described above can also be adopted.

In addition, while the flowcharts used in the above explanations describe multiple steps (processes) in a sequential order, the order in which the steps are performed in each embodiment is not limited to the order described. In each embodiment, the order of the steps shown in the figures can be changed to the extent that the content is not affected. Furthermore, the above-described embodiments and variations can be combined to the extent that the content is not contradictory.

Some or all of the above embodiments may also be described as, but are not limited to, the following notes:

1.
a data acquisition means for acquiring target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
and a trail generation means for generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition.
2.
the target image further includes a third region representing characters included in the document;
The evidence management device according to 1., wherein the evidence generation means calculates the sharpness based on the third region.
3.
3. The evidence management device according to 2 above, wherein the evidence data includes information indicating characters recognized from the image of the third area.
4.
The trail management device according to any one of 1. to 3., wherein the trail generation means, when determining that the first condition is satisfied, determines whether the clarity satisfies a second condition.
5.
the second condition is defined by a relationship between the sharpness and a second reference value,
5. The evidence management device described in any one of 1. to 4., wherein the evidence generation means holds candidate data including a plurality of candidate values that are candidates for the second reference value, and repeatedly determines whether or not the clarity of the target image satisfies a second condition by using, as the second reference value, a candidate value selected from the plurality of candidate values in descending order.
6.
The evidence management device described in 5. above, further comprising a preparation means for acquiring a plurality of reference data items including at least images of characters, and determining the plurality of candidate values based on the sharpness of each of the images included in the acquired plurality of reference data items.
7.
7. The evidence management device according to claim 5 or 6, wherein the target image data is a plurality of pieces of target image data, and the candidate data is data in which a maximum number of times a process for determining whether the sharpness of the target image satisfies a second condition is associated with each of the plurality of candidate values.
8.
8. The trail management device according to item 7 above, wherein the maximum number of executions is greater as the candidate value is smaller.
9.
The trail management device described in 7. or 8. above, wherein the trail generation means determines that the sharpness is equal to or greater than the second reference value using one of the candidate values as a second reference value, or repeatedly executes the process of determining whether the sharpness is equal to or greater than the second reference value until the second determination process associated with the candidate value has been repeated a maximum number of times.
10.
The target image data includes one or more target images,
9. The evidence management device according to any one of 1. to 9., wherein the evidence generation means generates confirmation-required data based on the one target image or any of the plurality of target images when the sharpness of all of the one or plurality of target images does not satisfy a second condition.
11.
The trail management device according to any one of 1. to 10. above;
a terminal device that takes an image of the person and the document to generate the target image data and transmits the target image data to the trail management device.
12.
The computer
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
generating evidence data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition.
13.
On the computer,
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
A program for executing the step of generating evidence data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition.
14.
On the computer,
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
A recording medium having a program recorded thereon for generating evidence data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition.

This application claims priority based on Japanese Patent Application No. 2022-017944, filed February 8, 2022, the disclosure of which is incorporated herein in its entirety.

100, 200, 300, 400, 500 Trail management system 101, 201 Terminal device 102, 202, 302, 402, 502 Server device 103 Data acquisition unit 104, 204, 304, 404 Trail generation unit 205 Photography unit 209 Trail data storage unit 210 Confirmation-required data storage unit 211 First judgment unit 212, 312, 412 Second judgment unit 213 Storage control unit 214 Notification unit 315, 415 Candidate holding unit 316, 416 Candidate data 517 Preparation unit P1 Target image PP Preview image AR1 First area AR2 Second area AR3 Third area G1, G2 Guide

Claims (9)

a data acquisition means for acquiring target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
a trail generating means for generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
The trail generation means holds candidate data including a plurality of candidate values that are candidates for the second reference value, and repeatedly determines whether the clarity of the target image satisfies the second condition by using the candidate value selected from the plurality of candidate values in descending order as the second reference value . the target image further includes a third region representing characters included in the document;
The evidence management device according to claim 1 , wherein the evidence generation means calculates the sharpness based on the third region. The evidence management device according to claim 1 or 2, wherein the evidence generation means, when determining that the first condition is satisfied, determines whether the clarity satisfies a second condition. The evidence management device of claim 1 or 2 further comprises a preparation means for acquiring a plurality of reference data including at least images of characters, and determining the plurality of candidate values based on the sharpness of each of the images included in the acquired plurality of reference data. The evidence management device of claim 1 or 2, wherein the target image data is multiple, and the candidate data is data in which the maximum number of times a process for determining whether the sharpness of the target image satisfies a second condition is associated with each of the multiple candidate values. The trail management device according to claim 5 , wherein the maximum number of executions is greater as the candidate value is smaller. The trail management device according to claim 1 or 2;
a terminal device that takes an image of the person and the document to generate the target image data and transmits the target image data to the trail management device. The computer
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
The generating of the evidence data includes repeatedly determining whether the clarity of the target image satisfies a second condition by using a candidate value selected from the plurality of candidate values in descending order as the second reference value based on candidate data including a plurality of candidate values that are candidates for the second reference value . On the computer,
Acquire target image data including a target image including a first area indicating the person and a second area indicating the person's image included in the document;
generating trail data based on the target image when the first area and the second area satisfy a first condition and the clarity of the target image satisfies a second condition ;
the second condition is defined by a relationship between the sharpness and a second reference value,
The program includes repeatedly determining whether the clarity of the target image satisfies a second condition by using a candidate value selected from the plurality of candidate values in descending order as the second reference value based on candidate data including a plurality of candidate values that are candidates for the second reference value .