Information processing device, information processing method, and information processing program

Abstract

To detect misuse using existing equipment and provide station staff with information that allows them to easily determine whether or not misuse has occurred. [Solution] An information processing apparatus according to an embodiment comprises: an information acquisition unit that acquires image information from a camera that photographs a gate; a fraudulent use analysis control unit that detects whether a user passing through the gate has committed fraud based on the image information; a face image extraction unit that extracts the face image of a user in whom fraudulent use has been detected based on the image information; a list generation unit that generates a list of fraudulent users including the time of the fraudulent use, gate identification information, the type of fraudulent use detected, and the face image of the user; and a communication control unit that transmits the list of fraudulent users.

Description

Technical Field

[0001] Embodiments of the present invention relate to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] An automatic ticket gate system is a system that has been used for many years to use railways. Due to being used for many years, there are many cases of illegal use during passage. From the perspective of users who are using it correctly, there is a situation where it may be perceived that the railway operator is neglecting illegal use.

[0003] Generally, the final determination of whether it is illegal use is made by visual inspection by a person such as a station staff member. However, in railway operators, there is also a decrease in station staff, etc., and there is a tendency for stations to become unmanned. Therefore, there is a limit to the ability of station staff to visually confirm and respond to illegal use. Therefore, various techniques for preventing illegal use have been proposed.

[0004] For example, in Patent Document 1, a technique is disclosed for collecting ticket information indicating a facility use ticket from a mobile terminal possessed by a user present at an entrance of a facility and detecting an illegal user of the facility based on the ticket information.

[0005] Also, in Patent Document 2, a technique is disclosed for accurately determining whether an entrant has appropriately presented a ticket of a type corresponding to the personal attributes of the entrant, so that a staff member can accurately confirm whether there is any illegality in the entry.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Patent Document 2

Summary of the Invention

[0007] There are concerns that the increasing automation of train stations will lead to an increase in fraudulent use, but introducing a new system to detect fraudulent use would be costly and time-consuming. Furthermore, it is technically difficult to detect 100% of fraudulent use, and new methods of fraud will need to be addressed. In addition, since station staff will ultimately need to visually determine whether or not fraudulent use has occurred, there is also the problem that introducing a new system would not be cost-effective.

[0008] This invention was made in view of the above circumstances and aims to provide a technology that can detect misuse using existing equipment and provide information that allows station staff to easily determine whether or not misuse has occurred. [Means for solving the problem]

[0009] The information processing device according to the embodiment includes: an information acquisition unit that acquires image information from a camera that photographs a gate; a fraudulent use analysis control unit that detects whether a user passing through the gate has committed fraud based on the image information; a face image extraction unit that extracts a face image of the user in whom fraudulent use has been detected based on the image information; a list generation unit that generates a list of fraudulent users that includes the time of the fraudulent use, the identification information of the gate, the type of fraudulent use detected, and the face image of the user; and a communication control unit that transmits the list of fraudulent users. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 shows an example of a schematic configuration of a fraud detection support system according to one embodiment. [Figure 2] Figure 2 is a block diagram showing an example of the hardware configuration of a central server according to one embodiment and the software configuration associated with said hardware configuration. [Figure 3]Figure 3 is a flowchart showing an example of the operation of generating a list for detecting fraudulent use by a central server according to one embodiment. [Figure 4] Figure 4 is a flowchart illustrating the process of step ST103 according to one embodiment in more detail. [Figure 5] Figure 5 shows an example of a list of fraudulent users displayed on the display of a railway operator's terminal. [Modes for carrying out the invention]

[0011] The information processing device, information processing method, and information processing program will be described in detail below with reference to the drawings. In the following embodiments, parts with the same number are assumed to perform the same operation, and therefore repeated explanations will be omitted. For example, when there are multiple identical or similar elements, a common code may be used to describe each element without distinction, or a sub-number may be used in addition to the common code to describe each element separately.

[0012] [Embodiment] (composition) Figure 1 shows an example of a schematic configuration of a fraud detection support system according to one embodiment. As shown in Figure 1, the fraud detection support system comprises a central server 1, station equipment 2, and a railway operator terminal 3. The station equipment 2 also includes automatic ticket gates 21 and surveillance cameras 22, etc.

[0013] The central server 1 operates as an information processing device that can be implemented using one or more computers. The central server 1 is installed at a designated location of the railway operator. For example, the central server 1 can be connected via a network to station equipment 2 such as automatic ticket gates 21 and surveillance cameras 22, and railway operator terminals 3, either by wired or wireless connection, and transmits and receives various types of information with these devices.

[0014] The automatic ticket gate 21 is a device that can be implemented with one or more computers and functions as a gate. The computers here include embedded systems that include hardware such as microcomputer chips, LSIs (Large Scale Integrations), ASICs (Application Specific Integrated Circuits), or FPGAs (Field-Programmable Gate Arrays).

[0015] The automatic ticket gate 21 controls whether to allow a user (passenger) to pass through the passage (enter or exit). One or more automatic ticket gates 21 are installed at ticket gates in places such as train stations. For example, each automatic ticket gate 21, acting as a gate, forms a passage through which users can pass. When a user passes through, the automatic ticket gate 21 generates and stores a passage log that includes the time the user passed through, sensor information detected by sensors installed in the automatic ticket gate 21, and ticket information read from the ticket held by the user. The automatic ticket gate 21 can also connect to a central server 1 via a network or wired connection, and sends and receives various information with the central server 1.

[0016] The surveillance camera 22 is a camera that can be implemented with one or more computers. The surveillance camera 22 is installed in a designated location within the station premises. For example, the surveillance camera 22 photographs the passageway formed by the automatic ticket gate 21. The surveillance camera 22 stores image information, including the images it has taken of the passageway. The surveillance camera 22 can also connect to the central server 1 via a network or wired connection, and sends and receives various information with the central server 1.

[0017] The railway operator terminal 3 is a device that can be realized by one or more computers. The railway operator terminal 3 is arranged, for example, in a room where station staff who check for unauthorized use wait. For example, the railway operator terminal 3 may be a staff machine capable of performing operations such as issuing train tickets, or may be any terminal such as a stationary or portable computer, a mobile terminal, a tablet terminal, a wearable terminal, etc. The railway operator terminal 3 includes a display for displaying the list generated by the central server 1. The station staff determines whether the user is using it illegally based on the information in the list. The details of the list will be described later. Also, the railway operator terminal 3 can be connected to the central server 1 via a network or by wire, and transmits and receives various information to and from the central server 1.

[0018] Here, the unauthorized use detection support system according to one embodiment is, for example, a system for detecting unauthorized use of entry and exit in transportation such as railways. However, it is needless to say that the unauthorized use detection support system is also applicable to the detection of unauthorized use of entry and exit in theme parks, live venues, event venues, etc.

[0019] FIG. 2 is a block diagram showing an example of the hardware configuration of the central server 1 according to one embodiment and the software configuration associated with the hardware configuration. The central server 1 includes a control unit 11, a program storage unit 12, a data storage unit 13, a communication interface 14, and an input / output interface 15. The control unit 11, the program storage unit 12, the data storage unit 13, the communication interface 14, and the input / output interface 15 are communicably connected to each other via a bus, a serial interface, or the like. Also, the input / output interface 15 is communicably connected to an input unit 151, an output unit 152, etc.

[0020] The control unit 11 of the central server 1 has a function of controlling the operation of the entire central server 1. The control unit 11 may include an internal cache and various interfaces. The control unit 11 realizes various processes by executing a program prestored in the internal cache or the program storage unit 12. For example, the control unit 11 is realized by a CPU (Central Processing Unit). Note that the control unit 11 may also be realized by hardware such as an LSI, an ASIC, or an FPGA.

[0021] As a storage medium, the program storage unit 12 can be used in combination with a non-volatile memory such as an EPROM (Erasable Programmable Read Only Memory), an HDD (Hard Disk Drive), or an SSD (Solid State Drive) that can be written and read at any time, and a non-volatile memory such as a ROM (Read Only Memory). The program storage unit 12 stores programs necessary for executing various processes. That is, the control unit 11 can realize various controls and operations by reading and executing the programs stored in the program storage unit 12.

[0022] The data storage unit 13 is a storage that uses in combination a non-volatile memory such as an HDD or a memory card that can be written and read at any time and a volatile memory such as a RAM (Random Access Memory) as a storage medium. The data storage unit 13 is used to store data acquired and generated in the process of the control unit 11 executing a program to perform various processes.

[0023] The communication interface 14 includes one or more wired or wireless communication modules. For example, the communication interface 14 includes a communication module that connects via wire to the automatic ticket gate 21, the surveillance camera 22, and the railway operator terminal 3, etc. Alternatively, the communication interface 14 may also include a communication module that connects wirelessly to the automatic ticket gate 21, the surveillance camera 22, and the railway operator terminal 3, etc. In other words, the communication interface 14 can be any general communication interface that can communicate with the automatic ticket gate 21, the surveillance camera 22, and the railway operator terminal 3, etc., under the control of the control unit 11, and send and receive various types of information.

[0024] The input / output interface 15 is connected to the input unit 151, the output unit 152, etc. The input / output interface 15 is an interface that enables the transmission and reception of information between the input unit 151, the output unit 152, etc. The input / output interface 15 may be integrated with the communication interface 14. For example, the input / output interface 15 and at least one of the input unit 151, the output unit 152, etc. are wirelessly connected using short-range wireless technology, and this short-range wireless technology may be used to transmit and receive various types of information.

[0025] The input unit 151 may include, for example, a keyboard or pointing device for an administrator managing the central server 1 to input various information to the central server 1. The input unit 151 may also include a reader for reading data to be stored in the program storage unit 12 or the data storage unit 13 from a memory medium such as a USB memory, or a disk device for reading such data from a disk medium.

[0026] The output unit 152 includes a display that shows the video that the central server 1 should show to the administrator, and a printer for printing the information displayed on the display. The output unit 152 may also include a writer for writing the information stored in the data storage unit 13 to a memory medium such as a USB memory, and a disk drive.

[0027] Next, the software configuration of the control unit 11 will be described in more detail. The control unit 11 comprises an information acquisition unit 111, a fraudulent use analysis control unit 112, a face image extraction unit 113, a list generation unit 114, and a communication control unit 115.

[0028] The information acquisition unit 111 has the function of acquiring the passage log stored by the automatic ticket gate 21 via the communication interface 14. Furthermore, the information acquisition unit 111 has the function of acquiring image information stored by the surveillance camera 22 via the communication interface 14.

[0029] The fraudulent use analysis control unit 112 has the function of analyzing image information or traffic logs to determine whether or not fraudulent use has occurred by the user. Details of the determination method will be described later.

[0030] Here, examples of fraudulent use include, for instance, an adult using a child's ticket, a user crouching to be mistaken for a child, tailgating by passing directly behind a legitimate user, and sensor concealment by obscuring part of the sensor of the automatic ticket gate 21 to cause misreading. It should be noted that these are merely examples of fraudulent use, and of course, any voluntary fraudulent use is acceptable as long as it is recognized as fraudulent use by the railway operator.

[0031] The face image extraction unit 113 has the function of extracting a user's face image from the image information of a user suspected of fraudulent use by the fraudulent use analysis control unit 112.

[0032] The list generation unit 114 has the function of generating a list of detected fraudulent users. The list of detected fraudulent users includes at least the date and time when fraudulent use was detected, the identification information of the automatic ticket gate 21, the type of fraudulent use detected, and a facial image. The communication control unit 115 has the function of transmitting a list of detected fraudulent users to the railway operator terminal 3 via the communication interface 14.

[0033] Next, the software configuration of the data storage unit 13 will be described in more detail. The data storage unit 13 comprises an acquired information storage unit 131 and a list storage unit 132. The acquired information storage unit 131 has the function of storing traffic logs and image information acquired by the information acquisition unit 111. The list storage unit 132 has the function of storing the list of fraudulent users detected by the list generation unit 114.

[0034] (operation) Figure 3 is a flowchart showing an example of the operation by the central server 1 according to one embodiment for generating a list for detecting unauthorized use. The operation of this flowchart is achieved by reading and executing a program stored in the internal cache of the control unit 11 of the central server 1 or in the program storage unit 12.

[0035] First, the automatic ticket gate 21 stores a pass-through log that includes the ticket ID of the user when they pass through, sensor information detected using the sensors installed in the automatic ticket gate 21, and so on. Furthermore, the surveillance camera 22 stores image information that includes image data of the user who passed through the automatic ticket gate 21.

[0036] First, this flowchart begins when the central server 1 sends data requests to the automatic ticket gate 21 and the surveillance camera 22, respectively, requesting traffic logs and image data.

[0037] In step ST101, the information acquisition unit 111 acquires image information. The information acquisition unit 111 acquires image information from the surveillance camera 22 through the communication interface 14. The information acquisition unit 111 stores the acquired image information in the acquired information storage unit 131.

[0038] In step ST102, the information acquisition unit 111 acquires a passage log. The information acquisition unit 111 acquires the passage log from the automatic ticket gate 21 via the communication interface 14. The information acquisition unit 111 stores the acquired passage log in the acquired information storage unit 131.

[0039] Note that steps ST101 and ST102 may be processed in any order, or they may be processed simultaneously.

[0040] Furthermore, the information acquisition unit 111 may acquire passage logs and image information in real time each time a user passes through the automatic ticket gate 21. Alternatively, the information acquisition unit 111 may acquire passage logs and image information at night when railway vehicles are not in operation, that is, during the period when the night batch is being performed. It should be noted that the timing of acquiring this information is not limited to the timings described above, but may be at any time. In addition, the information acquisition unit 111 may acquire this information in response to a request from a station employee using the railway operator terminal 3.

[0041] In step ST103, the fraudulent use analysis control unit 112 analyzes whether or not fraudulent use has occurred. The fraudulent use analysis control unit 112 acquires image information and traffic logs from the acquired information storage unit 131. Then, the fraudulent use analysis control unit 112 detects whether or not there has been fraudulent use by a user based on the image information or traffic logs. The fraudulent use analysis control unit 112 may perform this detection in real time or during a night batch process.

[0042] Figure 4 is a flowchart illustrating the process of step ST103 according to one embodiment in more detail. In step ST201, the fraudulent use analysis control unit 112 analyzes the image information. For example, the fraudulent use analysis control unit 112 determines whether an adult user is crouching to pass through, and if it determines that the user is crouching, it detects that fraudulent use has occurred. Alternatively, the fraudulent use analysis control unit 112 determines the distance between users, and if it is less than a predetermined distance, it detects that tailgating fraud has occurred. Alternatively, the fraudulent use analysis control unit 112 may use a model trained by machine learning or the like to analyze the image information and detect whether a user has committed fraud. Furthermore, the fraudulent use analysis control unit 112 identifies the automatic ticket gate 21 that detected the fraudulent use and generates identification information indicating which automatic ticket gate 21 the fraudulent use occurred at.

[0043] Here, since any general supervised learning method is acceptable for creating machine learning models, a detailed explanation will be omitted. For example, the data used for machine learning can be based on actual image information that has been misused.

[0044] In step ST202, the fraudulent use analysis control unit 112 determines whether fraudulent use has been detected. If fraudulent use is detected as a result of the image information analysis, the fraudulent use analysis control unit 112 outputs the image information in which fraudulent use was detected to the face image extraction unit 113. Furthermore, the fraudulent use analysis control unit 112 outputs to the list generation unit 114 the analysis results, which include the detection of fraudulent use based on the image information, the image information, and the identification information of the automatic ticket gate 21. Then, the process proceeds to step ST203. On the other hand, if fraudulent use is not detected as a result of the image information analysis, the process proceeds to step ST204.

[0045] In step ST203, the face image extraction unit 113 extracts face images. The face image extraction unit 113 extracts face images of users suspected of misuse from the image information. For example, the face image extraction unit 113 detects a user from the image information received from the misuse analysis control unit 112 and extracts the face image of that user. The face image extraction unit 113 outputs the extracted face images to the list generation unit 114.

[0046] In step ST204, the fraudulent use analysis control unit 112 analyzes the traffic log. For example, based on the traffic log, the fraudulent use analysis control unit 112 detects logs of the child's lamp being lit and determines the user's height based on the image information for that time period. The fraudulent use analysis control unit 112 also detects whether the ticket information is for adults or children. For example, if the fraudulent use analysis control unit 112 detects that fraudulent use has occurred for a user who is of adult height but was using a child's ticket. Alternatively, the fraudulent use analysis control unit 112 may analyze the traffic log using a model trained by machine learning or the like to detect whether fraudulent use has occurred for a user.

[0047] The method for creating machine learning models can be any general supervised learning method, so a detailed explanation is omitted here. For example, the data used for machine learning can be based on actual traffic logs of fraudulent activities.

[0048] In step ST205, the fraudulent use analysis control unit 112 determines whether fraudulent use has been detected. If fraudulent use is detected as a result of the analysis of the traffic log, the fraudulent use analysis control unit 112 outputs to the list generation unit 114 that fraudulent use has been detected based on the traffic log and the analysis results including the traffic log. Then, the process proceeds to step ST206. On the other hand, if fraudulent use is not detected as a result of the analysis of the traffic log, the process proceeds to step ST208.

[0049] In step ST206, the fraudulent use analysis control unit 112 determines whether it can identify the fraudulent user. If the fraudulent use analysis control unit 112 detects fraudulent use from the passage log, it acquires image information of the automatic ticket gate 21 corresponding to the time indicated in the passage log. Then, based on the image information, the fraudulent use analysis control unit 112 determines whether it can identify the user suspected of fraudulent use. For example, if the passage log detects tailgating, where a fraudulent user is passing right behind a legitimate user, the fraudulent use analysis control unit 112 determines, based on the image information, whether the users are connected. If it determines that the users are connected, the fraudulent use analysis control unit 112 identifies the user immediately behind the legitimate user as the fraudulent user. On the other hand, for example, if the users are not connected, that is, if the automatic ticket gate 21 detects tailgating but cannot detect the user in question, the fraudulent use analysis control unit 112 determines that it cannot identify the fraudulent user.

[0050] If a fraudulent user is identified, the fraudulent use analysis control unit 112 outputs the image information of the fraudulent user to the face image extraction unit 113. Furthermore, the fraudulent use analysis control unit 112 outputs the image information used to identify the fraudulent user to the list generation unit 114. Then, the process proceeds to step ST207. On the other hand, if a fraudulent user cannot be identified, the process proceeds to step ST208.

[0051] In step ST207, the face image extraction unit 113 extracts face images. The face image extraction unit 113 extracts face images of users suspected of misuse from the image information. For example, the face image extraction unit 113 detects a user from the image information received from the misuse analysis control unit 112 and extracts the face image of that user. The face image extraction unit 113 outputs the extracted face images to the list generation unit 114.

[0052] In step ST208, the fraudulent use analysis control unit 112 determines whether the analysis of all information has been completed. If it determines that the analysis of all information has been completed, the process proceeds to step ST104. On the other hand, if it determines that the analysis of all information has not been completed, the process proceeds to step ST201.

[0053] Returning to Figure 3, in step ST104, the list generation unit 114 generates a list of detected fraudulent users. The list generation unit 114 generates the list of detected fraudulent users based on the image information, traffic logs, and face images received from the fraudulent use analysis control unit 112 and the face image extraction unit 113, respectively. The list generation unit 114 stores the list of detected fraudulent users in the list storage unit 132. The list generation unit 114 may also include video information of when fraudulent use occurred by the user in the list of detected fraudulent users.

[0054] In step ST105, the communication control unit 115 transmits the list of detected fraudulent users. The communication control unit 115 periodically retrieves the list of detected fraudulent users from the list storage unit 132 or in response to a request from a station employee using the railway operator terminal 3, and transmits the list of detected fraudulent users to the railway operator terminal 3 via the communication interface 14. The railway operator terminal 3 then displays the received list of detected fraudulent users on its display.

[0055] Figure 5 shows an example of a list of fraudulent users displayed on the display of the railway operator terminal 3. As shown in Figure 5, the following are displayed on the screen as a list of detected fraudulent users: detection date and time (simply displayed as date and time in Figure 5), detection location, suspected fraud hit content, detection information, and a facial image. As shown in Figure 5, the detection location indicates which automatic ticket gate 21 detected the fraud. The suspected fraud hit content indicates what type of fraudulent use was detected. The detection information indicates whether the fraudulent use was detected from image information acquired from the surveillance camera 22 or from the passage log acquired from the automatic ticket gate 21. The facial image displayed is a facial image extracted by the facial image extraction unit 113. Note that if a fraudulent user could not be identified in step ST206, the facial image field may be left blank.

[0056] The station staff at the railway operator terminal 3 will make a final determination on whether a user actually committed fraud based on this fraudulent user detection list. If the fraudulent user detection list includes video information, the station staff may, of course, refer to the video information. In addition, the railway operator terminal 3 may display a video thumbnail instead of, or simultaneously with, the facial image.

[0057] (Effects of the embodiment) According to the embodiment described above, the central server 1 determines whether a user is a fraudulent user who has committed fraudulent activity, based on image information or traffic logs. The central server 1 then creates a list of fraudulent users suspected of fraudulent activity and transmits this list to the railway operator terminal 3. Station staff using the railway operator terminal 3 verify whether a user has committed fraudulent activity based on the list of fraudulent users. Since station staff can verify users suspected of fraudulent activity over a certain period all at once, the verification time can be reduced.

[0058] [Other embodiments] It should be noted that this invention is not limited to the above-described embodiments. For example, in the embodiments, an example was described in which a station employee using the railway operator terminal 3 performs a check for fraudulent use based on the fraudulent user detection list. However, an administrator managing the central server 1 may also perform a check for fraudulent use based on the fraudulent user detection list. In this case, instead of the central server 1 transmitting the fraudulent user detection list to the railway operator terminal 3, it may simply display the fraudulent user detection list on the output unit 152.

[0059] In short, this invention is not limited to the embodiments described above, and can be modified in various ways during implementation without departing from its essence. Furthermore, each embodiment may be combined as appropriately as possible, and in that case, the combined effects can be obtained. Moreover, the embodiments described above include inventions at various stages, and various inventions can be extracted by appropriate combinations of the multiple constituent elements disclosed. [Explanation of Symbols]

[0060] 1…Central Server 2… Station equipment 3…Railway operator terminals 11…Control Unit 111…Information acquisition department 112...Fraudulent Use Analysis Control Unit 113... Face image extraction unit 114...List generation unit 115...Communication Control Unit 12…Program memory 13…Data storage unit 131...Acquired information storage unit 132... List memory section 14…Communication Interface 15… Input / Output Interface 151...Input section 152…Output section 21... Automatic ticket gates 22… Surveillance cameras

Claims

1. An information acquisition unit that acquires image information from a camera that photographs the gate, An unauthorized use analysis control unit that detects whether there has been any unauthorized use by a user passing through the gate based on the aforementioned image information, A face image extraction unit extracts the face image of the user whose misuse was detected based on the aforementioned image information, A list generation unit generates a list of fraudulent users, including the time of the fraudulent use, the gate identification information, the type of fraudulent use detected, and the user's facial image. A communication control unit that transmits the aforementioned list of detected fraudulent users, An information processing device equipped with the following features.

2. The information acquisition unit further acquires the passage log acquired by the gate when the user passes through the gate, If the fraudulent use analysis control unit fails to detect fraudulent use based on the image information, it further detects whether the user has engaged in fraudulent use based on the traffic log. The information processing apparatus according to claim 1.

3. If the fraudulent use analysis control unit determines, based on the traffic log, that the user has engaged in fraudulent use, it determines whether the user can be identified based on the image information. If the facial image extraction unit determines that it can identify the user, it extracts the user's facial image. The information processing apparatus according to claim 2.

4. The aforementioned list of detected fraudulent users further includes detection information indicating whether fraudulent activity was detected in the image information or the traffic log. The information processing apparatus according to claim 2 or 3.

5. The aforementioned list of detected fraudulent users includes video information from when the fraudulent activity was detected. The information processing apparatus according to claim 1.

6. The aforementioned gate is an automatic ticket gate, The aforementioned information acquisition unit acquires the image information at night when railway vehicles are not in operation. The aforementioned misuse analysis control unit performs the detection during the night batch. The information processing apparatus according to claim 1.

7. An information processing method executed by the processor of an information processing device, To acquire image information from the camera that photographs the gate, Based on the aforementioned image information, the system detects whether or not there has been any misuse by the user passing through the gate. Based on the aforementioned image information, the facial image of the user whose misuse was detected is extracted, The process involves generating a list of fraudulent users, including the time of the fraudulent activity, the gate's identification information, the type of fraudulent activity detected, and the user's facial image. The aforementioned list of detected fraudulent users is to be transmitted, An information processing method comprising:

8. An information processing program comprising instructions to be executed by the processor of an information processing device. To acquire image information from the camera that photographs the gate, Based on the aforementioned image information, the system detects whether or not there has been any misuse by the user passing through the gate. Based on the aforementioned image information, the facial image of the user whose misuse was detected is extracted, The process involves generating a list of fraudulent users, including the time of the fraudulent activity, the gate's identification information, the type of fraudulent activity detected, and the user's facial image. The aforementioned list of detected fraudulent users is to be transmitted, An information processing program equipped with the necessary components.

Images