JP5560976B2 - Information processing apparatus and method, and program - Google Patents

Description

  The present invention relates to an information processing apparatus, method, and program, and more particularly, to an information processing apparatus, method, and program capable of reducing false detection such as recognizing an object other than a person as a person in authentication processing using a face image. About.

  Currently, in many stores and public facilities, information on an unspecified number of customers or visitors is captured as an image, and a face image of a person in the captured image is registered in advance, for example, a wanted crime. A technique for collating with a human face image has been proposed (see Patent Document 1).

JP 2002-279540 A

  However, in Patent Document 1, since the vicinity of the entrance / exit is an imaging region, when face images other than a human image such as a character image drawn on a poster or a standing signboard are present in the imaging region, In some cases, it was erroneously detected as a human face image.

  In order to deal with such erroneous detection, a technique for reducing erroneous detection by masking an image region that recognizes a face image other than a person has been proposed (see Japanese Patent Application No. 2007-156893).

  However, even if masking is performed in this manner, erroneous detection cannot always be effectively reduced when an object other than a person moves.

  The present invention has been made in view of such a situation, and the reliability of the face image and the reliability of each organ constituting the face in the face image are obtained, and they are reliable higher than a predetermined value. The authentication process is executed only when the image is an object to reduce false detection in the authentication process using the face image.

  An information processing apparatus according to an aspect of the present invention includes an accumulation unit that accumulates a face image as an accumulator face image in an accumulator database, an acquisition unit that acquires a face image of a person to be collated from an image, and an acquisition unit that acquires the face image The matching means for calculating and collating the similarity between the face image of the person to be collated and the face image of the accumulator accumulated in the accumulating means, the similarity as the collation result of the collating means, and a predetermined threshold And a similarity determination unit that determines whether the face image of the person to be collated is the face image of the accumulator, and the face image of the person to be collated by the accumulator by the similarity determination unit And a face for calculating the reliability of the face image acquired by the acquisition means as the face image reliability when it is determined that the person to be collated is the accumulator. Image reliability calculating means, and Organ extracting means for extracting a facial organ from the face image acquired by the obtaining means, organ reliability calculating means for calculating the reliability of the organ extracted by the organ extracting means as organ reliability, and the face image trust When both the degree and the organ reliability are higher than a predetermined threshold, the collation unit and the reliability determination unit that controls the similarity determination unit to perform processing.

  The obtaining means obtains a face image from the image using template matching using a template corresponding to the contour shape of the face, template matching based on an organ constituting the face, and a head constituting the face by chroma key processing. After detecting the vertices of the part, a method of detecting a face image based on the vertices, a method of detecting based on the shape of the skin color region, or a method of detecting a face-like region by learning with a teacher signal based on a neural network Can be included.

  The face image reliability calculation means calculates a value indicating the degree of matching with the template as the face image reliability by template matching using a template corresponding to the face outline based on the face image. You can make it.

  Based on the face image, the organ extracting means extracts pixel position information constituting the organ as a feature point, and a feature point comprising pixel position information constituting the organ including the outline of the face image By pattern matching using a pattern obtained by learning the arrangement of the pattern, or pattern matching based on the arrangement pattern of feature points consisting of information on pixel positions constituting the organ in the region of the face image Can be.

  The organ reliability calculation means is extracted by the organ extraction means and a vector of luminance values of pixels at pixel positions that are pattern of feature points constituting the organ in the face image obtained by learning by a teacher signal. It is possible to calculate the distance in the Euclidean space from the brightness value vector of the pixel at the pixel position that becomes the pattern of the feature points constituting the organ as the organ reliability.

  An information processing method according to one aspect of the present invention includes an accumulating unit that accumulates a face image as an accumulator's face image in an accumulator database, an acquisition unit that acquires a face image of a collation target person from the image, and an acquisition unit that acquires the face image. The matching means for calculating and collating the similarity between the face image of the person to be collated and the face image of the accumulator accumulated in the accumulating means, the similarity as the collation result of the collating means, and a predetermined threshold And a similarity determination unit that determines whether the face image of the person to be collated is the face image of the accumulator, and the face image of the person to be collated by the accumulator by the similarity determination unit And a face for calculating the reliability of the face image acquired by the acquisition means as the face image reliability when it is determined that the person to be collated is the accumulator. Image reliability calculating means, and Organ extracting means for extracting a facial organ from the face image acquired by the obtaining means, organ reliability calculating means for calculating the reliability of the organ extracted by the organ extracting means as organ reliability, and the face image trust And a reliability determination unit that controls the collation unit and the similarity determination unit to perform processing when both the degree and the organ reliability are higher than a predetermined threshold. An information processing method, wherein the obtaining unit obtains a face image of a person to be collated from an image in the obtaining unit, the face image of the person to be collated obtained by the process of the obtaining step in the collating unit, A collation step for calculating and collating the similarity with the accumulator face image accumulated in the accumulating means, and a collation result in the collation step processing in the similarity determining means A similarity determination step for determining whether or not the face image of the person to be collated is the face image of the accumulator by comparing a certain similarity with a predetermined threshold value, and the similarity determination step in the notification means A notification step of notifying that the person to be collated is the accumulator when the face image of the person to be collated is determined to be the face image of the accumulator by the processing of A face image reliability calculation step of calculating the reliability of the face image acquired by the process of the acquisition step as a face image reliability, and a face image acquired by the process of the acquisition step in the organ extraction means An organ extraction step for extracting a facial organ, and the reliability of the organ extracted by the processing of the organ extraction step in the organ reliability calculation means is used as the organ reliability. When both of the organ reliability calculation step to calculate and the face image reliability and the organ reliability in the reliability determination means are higher than a predetermined threshold, the collation step and the similarity determination step And a reliability determination step for controlling to execute the process.

  The program according to one aspect of the present invention is acquired by an accumulating unit that accumulates a face image as an accumulator's face image in an accumulator database, an acquisition unit that acquires a face image of a person to be collated from an image, and the acquisition unit. The similarity between the face image of the person to be collated and the face image of the accumulator accumulated in the accumulating means is calculated, the collation means for collating, the similarity as the collation result of the collating means and a predetermined threshold value Similarity determination means for determining whether the face image of the person to be collated is the face image of the accumulator by comparison, and the face image of the person to be collated is the face of the accumulator by the similarity determination means If it is determined that the image is an image, a notifying unit that notifies that the person to be collated is the accumulator, and a face image trust that calculates the reliability of the face image acquired by the acquiring unit as a face image reliability Degree calculation means, Organ extracting means for extracting a facial organ from the face image acquired by the means, organ reliability calculating means for calculating the reliability of the organ extracted by the organ extracting means as organ reliability, and the face image reliability And when the organ reliability is higher than a predetermined threshold, the information processing apparatus is controlled including a reliability determination unit that controls the collation unit and the similarity determination unit to execute processing. In the acquisition unit, the acquisition unit acquires the facial image of the verification target person from the image, the verification unit acquires the facial image of the verification target in the verification unit, and the storage unit The collation step for calculating and collating the similarity with the accumulated face image of the accumulator, and the collation result in the collation step processing in the similarity determination means A similarity determination step for determining whether or not the face image of the person to be collated is the face image of the accumulator by comparing a certain similarity with a predetermined threshold value, and the similarity determination step in the notification means A notification step of notifying that the person to be collated is the accumulator when the face image of the person to be collated is determined to be the face image of the accumulator by the processing of A face image reliability calculation step of calculating the reliability of the face image acquired by the process of the acquisition step as a face image reliability, and a face image acquired by the process of the acquisition step in the organ extraction means An organ extraction step for extracting a facial organ, and the reliability of the organ extracted by the processing of the organ extraction step in the organ reliability calculation means is used as the organ reliability. When both of the organ reliability calculation step to calculate and the face image reliability and the organ reliability in the reliability determination means are higher than a predetermined threshold, the collation step and the similarity determination step The process including the reliability determination step of controlling to execute the process is executed.

  In one aspect of the present invention, the face image is accumulated in the accumulator database as the accumulator face image, the face image of the collation target person is acquired from the image, and the acquired face image of the collation target person is accumulated. The similarity with the accumulator face image is calculated, collated, and whether the collation target face image is the accumulator face image by comparing the similarity as a collation result with a predetermined threshold value. Is determined, and when it is determined that the face image of the person to be collated is the face image of the accumulator, it is notified that the person to be collated is the accumulator, and the reliability of the acquired face image Is calculated as the face image reliability, the organ of the face is extracted from the acquired face image, the reliability of the extracted organ is calculated as the organ reliability, and any one of the face image reliability and the organ reliability Is higher than a predetermined threshold, If processing, and the similarity determination process is controlled to be executed.

  In the information processing apparatus according to one aspect of the present invention, the accumulation unit that accumulates the face image as the accumulator face image in the accumulator database is, for example, a biometric information database, and acquires the face image of the person to be collated from the image. The acquisition unit is, for example, a face image acquisition unit, which calculates the similarity between the face image of the person to be verified acquired by the acquisition unit and the face image of the person accumulated in the accumulation unit, and performs collation For example, the collation means is a similarity calculation unit, and the face image of the person to be collated is the face image of the accumulator by comparing the similarity as the collation result of the collation means with a predetermined threshold. The similarity determination means for determining whether or not is, for example, a similarity determination unit, and when the similarity determination means determines that the face image of the person to be collated is the face image of the accumulator, The person to be verified The notification means for notifying that the person is a person is, for example, a communication unit, and the face image reliability calculation means for calculating the reliability of the face image acquired by the acquisition means as the face image reliability is, for example, The face image reliability calculation unit, and the organ extraction unit that extracts a facial organ from the face image acquired by the acquisition unit is, for example, a face organ extraction unit, and the organ extracted by the organ extraction unit The organ reliability calculation means for calculating the reliability as the organ reliability is, for example, a facial organ reliability calculation unit, and both the face image reliability and the organ reliability are higher than a predetermined threshold. In this case, the reliability determination unit that controls the collation unit and the similarity determination unit to execute processing is, for example, a face image determination unit.

  That is, both the face image reliability calculated by the face image reliability calculation unit and the organ reliability calculated by the organ reliability calculation unit are higher than a predetermined threshold, and the face image determination unit Whether or not the face images of the collation target person and the accumulator match by calculating the similarity by the similarity calculation unit and comparing the similarity with a predetermined threshold only in a situation where the reliability of the image is guaranteed to some extent. Judgment was made.

  As a result, for a face image with low face image reliability or low organ reliability, the similarity calculation unit does not perform collation processing, and it is determined whether the collation target person and the accumulator match. Therefore, it is possible to omit face image processing that originally does not require authentication processing on the face image, to reduce false detection, and to reduce processing load by eliminating unnecessary processing. Become.

  According to the present invention, it is possible to reduce erroneous detection in authentication processing using a face image.

It is a figure which shows the structure of one Embodiment of the monitoring system to which this invention is applied. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the camera of FIG. It is a figure which shows the example of installation of the entrance camera and shop camera of FIG. It is a figure explaining the structural example of the image processing unit of FIG. It is a figure explaining the structural example of the biometric information recognition apparatus of FIG. It is a flowchart explaining a registered player visit monitoring process. It is a flowchart explaining a reliability calculation process. It is a flowchart explaining a similarity calculation process. And FIG. 11 is a diagram illustrating a configuration example of a general-purpose personal computer.

  Embodiments of the present invention will be described below. Correspondences between the configuration requirements of the present invention and the embodiments described in the detailed description of the present invention are exemplified as follows. This description is to confirm that the embodiments supporting the present invention are described in the detailed description of the invention. Accordingly, although there are embodiments that are described in the detailed description of the invention but are not described here as embodiments corresponding to the constituent elements of the present invention, It does not mean that the embodiment does not correspond to the configuration requirements. Conversely, even if an embodiment is described here as corresponding to a configuration requirement, that means that the embodiment does not correspond to a configuration requirement other than the configuration requirement. It's not something to do.

  That is, the information processing apparatus according to one aspect of the present invention includes a storage unit (for example, the biometric information DB 22 in FIG. 10) that stores a face image in the store database as a store person's face image; The degree of similarity between the acquisition means (for example, the face image acquisition unit 221 in FIG. 10), the face image of the person to be collated acquired by the acquisition means, and the face image of the accumulator accumulated in the accumulation means The collation means (for example, the similarity calculation unit 232 in FIG. 10) that calculates and collates, and the similarity that is the collation result of the collation means and a predetermined threshold value, the face image of the person to be collated is Similarity determination means (for example, the similarity determination unit 235 in FIG. 10) for determining whether or not the face image of the accumulator is used, and the face image of the person to be collated is the face of the accumulator by the similarity determination means. When it is determined to be an image Notification means (for example, the communication unit 228 in FIG. 10) for notifying that the person to be collated is the accumulator, and a face image that calculates the reliability of the face image acquired by the acquisition means as the face image reliability Reliability calculation means (for example, face image reliability calculation section 222 in FIG. 10) and organ extraction means (for example, face organ extraction section 223 in FIG. 10) that extracts facial organs from the face image acquired by the acquisition means. ), Organ reliability calculation means (for example, facial organ reliability calculation unit 224 in FIG. 10) that calculates the reliability of the organ extracted by the organ extraction means as the organ reliability, and the face image reliability, and When any of the organ reliability levels is higher than a predetermined threshold, a reliability level determination unit (for example, a face image determination unit in FIG. 10) that controls the collation unit and the similarity determination unit to execute processing. 225).

  An information processing method according to one aspect of the present invention includes an accumulating unit that accumulates a face image as an accumulator's face image in an accumulator database, an acquisition unit that acquires a face image of a collation target person from the image, and an acquisition unit that acquires the face image. The matching means for calculating and collating the similarity between the face image of the person to be collated and the face image of the accumulator accumulated in the accumulating means, the similarity as the collation result of the collating means, and a predetermined threshold And a similarity determination unit that determines whether the face image of the person to be collated is the face image of the accumulator, and the face image of the person to be collated by the accumulator by the similarity determination unit And a face for calculating the reliability of the face image acquired by the acquisition means as the face image reliability when it is determined that the person to be collated is the accumulator. Image reliability calculating means, and Organ extracting means for extracting a facial organ from the face image acquired by the obtaining means, organ reliability calculating means for calculating the reliability of the organ extracted by the organ extracting means as organ reliability, and the face image trust And a reliability determination unit that controls the collation unit and the similarity determination unit to perform processing when both the degree and the organ reliability are higher than a predetermined threshold. An information processing method, which is acquired by an acquisition step (for example, step S21 in FIG. 11) of acquiring a face image of a person to be verified from an image in the acquisition unit and a process of the acquisition step in the verification unit. A collation step for calculating and collating the similarity between the face image of the person to be collated and the face image of the accumulator accumulated in the accumulation means (for example, step S73 in FIG. 13) Whether or not the face image of the person to be collated is the face image of the accumulator by comparing the similarity that is the collation result in the process of the collation step with a predetermined threshold in the similarity determination unit. A similarity determination step (for example, step S32 in FIG. 11) for determining whether or not the face image of the person to be collated is the face image of the accumulator by the processing of the similarity determination step in the notification means. The face image acquired by the notification step (for example, step S35 in FIG. 11) for notifying that the person to be collated is the accumulator and the acquisition step in the face image reliability calculation means. The face image reliability calculation step (for example, step S51 in FIG. 12) for calculating the reliability of the image as the face image reliability, and the processing of the acquisition step in the organ extraction means An organ extraction step (for example, step S52 in FIG. 12) for extracting a facial organ from the face image acquired by the processing, and the reliability of the organ extracted by the processing of the organ extraction step in the organ reliability calculation means The organ reliability calculation step (for example, step S53 in FIG. 12) for calculating the organ reliability as the organ reliability, and the face image reliability and the organ reliability in the reliability determination means are both lower than a predetermined threshold. If the value is also higher, a reliability determination step (for example, step S24 in FIG. 11) that controls to execute processing in the collation step and the similarity determination step is included.

[Example of monitoring system configuration]
FIG. 1 is a diagram showing a configuration of an embodiment of a game shop monitoring system according to the present invention.

  The game stores 1-1 to 1-n are so-called pachinko stores, pachislot stores, or casino stores. These amusement stores 1-1 to 1-n are affiliated stores or member stores of the biometric information management center or the third party amusement store management center, and stores that need to manage a plurality of stores in an integrated manner. It is. The game stores 1-1 to 1-n are connected by a biological information management bus 6 and a third-party game store management bus 7, and the public communication line networks 8 and 9 represented by the bus and the Internet are used. Via each other, biological information and third-party amusement store management information are exchanged. In the following description, when there is no particular need to distinguish each of the game stores 1-1 to 1-n, the game store 1 is simply referred to, and the other configurations are also referred to in the same manner.

  The biometric information management bus 6 mainly functions as a transmission path for distributing biometric information managed by the biometric information recognition device 21 of each game store 1. The third-party game store management bus 7 functions as a transmission path for distributing medium lending management information managed mainly by the medium lending management device 27 of each amusement store 1.

  The biometric information management center 2 is a server used by a business operator who manages and operates the biometric information management center. The registered player DB managed by the biometric information management database (hereinafter also referred to as DB) 3 is stored in the biometric information management center 2. In addition to updating based on the unregistered player DB generated by each gaming store 1, the updated updated registered player DB is distributed to the biometric information recognition device 21 of each gaming store 1.

  The third-party amusement store management center 4 is a server used by a business operator that manages and operates the third-party amusement store management center, and the medium lending management managed by the third-party amusement store management database (DB) 5 In addition to updating the information DB based on the information supplied from each game store 1, the updated latest medium lending management information is distributed to the medium lending management device 27 of each game store 1.

  The biometric information recognition apparatus 21 includes image processing units 39-1 to 39 based on images captured by the cameras 38-1 to 38-m, the entrance cameras 40-1 to 40-p, and the in-store cameras 41-1 to 41-q. Based on the face image information extracted by − (m + p + q) and supplied via the biometric information bus 31, the face image registered in advance in the biometric information database 22 is collated. The store visitor's visit is notified to the portable terminal 20 or displayed on the display unit 23 made up of an LCD (Liquid Crystal Display) or an organic EL (Electronic Luminescent). Further, when the face image registered in the biometric information database 22 is collated and does not match, the biometric information recognition device 21 accesses the biometric information management database 3 and registers it as an unregistered player in the unregistered player DB. .

  The game store management device 24 is a so-called hall computer, and monitors the operations of the game machines 36-1 to 36-m via the game store management information bus 30. The game store management device 24 determines a predetermined amount according to the monitoring status such as information on the payout of the game table 36 or the payout of medals, the call information of the players on the game tables 36-1 to 36-m, or the occurrence of an error. The above process is executed, and the execution result is displayed on the display unit 25 formed of an LCD, an organic EL, or the like. The game store management device 24 identifies information supplied from the counter 35, the game machines 36-1 to 36-m, and the game machine peripheral terminals 37-1 to 37-m, respectively. For example, it is managed by the gaming machine management database 26 in association with the gaming machine number.

  The medium lending management device 27 manages the medium lending management information of the rented game media using the medium lending management database 29 based on the information from the payment machine 33 and the lending machine 34, and also the medium lending management database. When the medium lending management information registered in 29 is updated, the updated information is sent to the third party amusement store management center 4 via the third party amusement store management bus 7 and the public communication line network 9. Further, the medium lending management device 27 acquires the medium lending management information supplied by the third party amusement store management center 4 via the third party amusement store management bus 7 and the public communication line network 9, and manages the medium lending management. Store in the database 29.

  The lending machine 34 lends a number of game media according to the amount when the player accepts a predetermined amount by cash or a prepaid card when playing on the gaming table 36. At this time, the lending machine 34 supplies information on the number of lent gaming media to the medium lending management device 27 together with information such as the accepted cash and the remaining number of prepaid cards. Thereby, the medium lending management device 27 registers information on the number of lent gaming media in the medium lending management information database 29 together with information such as the received cash and the remaining number of prepaid cards.

  The checkout machine 33 sells prepaid cards with a frequency for borrowing a ball. At this time, the checkout machine 33 supplies the medium lending management device 27 with the amount paid for the frequency of the prepaid card sold. Further, the settlement vending machine 33 settles and pays out cash based on the remaining number of game media lent as a frequency such as a prepaid card. At this time, the checkout machine 33 supplies the remaining number of prepaid cards and the amount of cash refunded to the medium lending management device 27.

  The counter 35 counts the number of game media acquired by the player playing on the game table 36, and outputs the counting result as a magnetic card or a receipt.

  The game machines 36-1 to 36-m execute a game when a predetermined operation is performed by the player, and pay out a game ball or a medal according to a so-called small hit or big hit.

  The game console peripheral terminals 37-1 to 37-m are so-called pedestrian machines provided corresponding to the respective game machines 36-1 to 36-m. The same as the lending machine 34) is provided. Further, the gaming machine peripheral terminal 37 acquires biological information such as a face image of the player who plays the gaming machine 36 and transmits it to the biological information recognition device 21 together with the gaming machine identification information (game machine number). FIG. 1 shows an example in which cameras 38-1 to 38-m that acquire a player's face image are provided as a function of acquiring biometric information.

  For example, as shown in FIG. 2, the cameras 38-1 to 38-m are arranged below the table display lamps 61-1 to 61-4 provided on the respective upper sides of the game tables 36-1 to 36-4. As shown in FIG. 3, a face image may be taken so that the player can take an image within the reading range δ. Can be used.

  Further, as shown in FIG. 4, the cameras 38-1 to 38-m are provided with convex portions 71-1 to 71-4 on the gaming machine peripheral terminals 37-1 to 37-4, as shown in FIG. As described above, it may be provided so that a player's face image can be captured within the reading range θ.

  Further, for example, as shown in FIG. 6, the cameras 38-1 to 38-m may be provided at the center of the game table 36 (on the board surface of the game table 36) to take an image. That is, by installing the camera 38 in the installation unit 81 of FIG. 6, the player is imaged within the reading range φ as shown in FIG.

  The entrance cameras 40-1 to 40-p and the in-store cameras 41-1 to 41-q are installed at entrances and predetermined places in the store of the amusement store 1, and imaged units 39- (m + 1) to 39- (m + p + q).

  The entrance cameras 40-1 to 40-p and the in-store cameras 41-1 to 41-q are set as shown in FIG. 8, for example. FIG. 8 shows an installation example of the entrance cameras 40-1 to 40-p and the in-store cameras 41-1 to 41-q in the game store 1.

  That is, in FIG. 8, entrances 112-1 to 112-3 are provided, and the entrance cameras 40-1 to 40-3 image players who enter the store from each entrance and exit 112. Further, the in-store cameras 41-1 to 41-10 are set at positions at which both surfaces of the island facilities 111-1 to 111-5 can be imaged in a row. The island facilities 111 are provided with game machines 36 on both sides, that is, so as to sandwich the island facilities 111 in the vertical direction in the figure. Since the camera 38, the entrance camera 40, and the in-store camera 41 all have a pan / tilt / zoom function, the in-store cameras 41-1 to 41-10 are arranged as shown in FIG. All players who play at 36 can take an image with any of the in-store cameras 41-1 to 41-10.

  Furthermore, the in-store camera 41-a is provided in front of the lending machine 34, the in-store camera 41-b is provided in front of the checkout machine 33, and the in-store camera 41-c is the counter of the counter 35. It is provided in front, and a player who uses the rental machine 34, the checkout machine 33, and the counter 35 can be imaged.

  That is, as shown in FIG. 8, in the game store 1, a player who visits the store, a player who plays on the game stand 36, and a player who uses the lending machine 34, the checkout machine 33, and the counter 35. The camera 38, the entrance camera 40, and the in-store camera 41 are installed so that almost all of the actions that the player is supposed to take in the gaming store 1 can be monitored.

[Configuration example of image processing unit]
Next, a configuration example of the image processing unit 39 will be described with reference to FIG.

  The image acquisition unit 201 acquires an image captured by the camera 38 (or the entrance camera 40 or the in-store camera 41) and supplies it to the face image extraction unit 202. The face image extraction unit 202 extracts the face image from the face image by template matching using a reference template corresponding to the contour of the entire face made up of patterns such as the arrangement of the parts constituting the face in the image supplied from the image acquisition unit 201. This rectangular image is extracted and supplied to the transmission unit 203. The transmission unit 203 accumulates the face image continuously supplied from the face image extraction unit 202 in the buffer 203a, and when the supply of the face image is stopped, the transmission unit 203 converts the face image accumulated in the buffer 203a to the face. It transmits to the biometric information recognition apparatus 21 as an image group. Since the capacity of the buffer 203a is limited, the transmission unit 203 transmits a series of continuously supplied face images as a face image group when the capacity that can be stored in the buffer 203a is full. Alternatively, a predetermined number may be determined in advance, and when the predetermined number is accumulated, the predetermined number of face images may be transmitted as a face image group.

[Configuration example of biological information recognition apparatus]
Next, a configuration example of the biological information recognition device 21 will be described with reference to FIG.

  The face image acquisition unit 221 acquires a face image group including a plurality of face images supplied from the image processing unit 39 and supplies the face image group to the face image reliability calculation unit 222. The face image reliability calculation unit 222 calculates the reliability of a plurality of face images included in the face image group supplied from the face image acquisition unit 221 as the face image reliability, and sends it to the face organ extraction unit 223 together with the face image. Supply. Here, the face image reliability is a value representing the reliability of the area constituting the acquired face image, and the higher the face image reliability, the more the face image is actually included in the area constituting the face image. Indicates a high probability of existence.

  The facial organ extraction unit 223 extracts feature points constituting each organ from the regions constituting the facial image acquired by the facial image acquisition unit 221 as facial organs, and the facial organ reliability together with the facial image reliability and the facial image group. This is supplied to the degree calculation unit 224. Here, the feature points that make up the facial organs are pixels at particularly characteristic positions among the pixels that make up the mouth, eyes, and nose organs, such as the ends of the mouth, the corners of the eyes, the eyes, and the tip of the nose. is there.

  The facial organ reliability calculation unit 224 calculates a value representing the reliability of facial organ information composed of pixel position information of feature points extracted by the facial organ extraction unit 223 as facial organ reliability, and the facial organ reliability The face image reliability and the face image group are supplied to the face image determination unit 225. Here, the facial organ reliability is a value indicating the reliability of the organ extracted by the facial organ extraction unit 223. The higher the facial organ reliability, the more characteristic points identified by the extracted facial organ are: This indicates that there is a high probability that the pixel is a characteristic position constituting an actual organ.

  The face image determination unit 225 has a facial image reliability and a facial organ reliability of each facial image constituting the facial image group supplied from the facial organ reliability calculation unit 224, both higher than a predetermined threshold value. Only when the acquired face image and the extracted facial organ are both reliable, they are supplied to the collation unit 226 together with the face image group. Therefore, the face image determination unit 225 determines that the face image reliability is lower than a predetermined threshold value and the reliability of the acquired face image itself is low, or the face organ reliability is lower than the predetermined threshold value. If the extracted face organ is low in reliability and may be a face image like a character even if the face image is acquired, the face image is supplied to the matching unit 226. do not do.

  That is, the face image is not supplied to the matching unit 226 when it cannot be regarded as a face image or when it is a face image but cannot be regarded as a face image of a real person such as a character. As a result, for face images that are not suitable for collation, the collation itself is not performed, so that erroneous detection can be reduced. Note that the face image determination unit 225 determines whether the face image reliability and the facial organ reliability exceed a predetermined threshold value or not among a plurality of face images constituting the face image group. If included, only those exceeding may be supplied to the collation unit 226, and if any of them does not exceed, all the face images of the face image group are not supplied to the collation unit 226. Also good.

  The collation unit 226 collates each face image of the face image group supplied from the face image determination unit 225 with the registered player's face image registered in advance in the biometric information DB 22, and determines a candidate having a high degree of similarity. If there is such a face image, the face images up to the third candidate are displayed on the display unit 23 as a matching result. The collation unit 226 supplies the supplied face image to the unregistered player database registration unit 227 when there is no face image that is a candidate with a high degree of similarity.

  More specifically, the feature amount extraction unit 231 of the collation unit 226 extracts a feature amount for identifying each face image in the face image group, and supplies the feature amount together with each face image to the similarity calculation unit 232. . The similarity calculation unit 232 extracts the feature amount of the registered player's face image registered in the biometric information DB 22, and at the same time extracts the feature amount of each face image of the face image group supplied from the feature amount extraction unit 231. Are sequentially used to obtain the similarity to the face images of all registered players registered in the biometric information DB 22, and together with the face images supplied from the face image acquisition unit 221, the similarity calculation result storage unit 233. More specifically, the similarity calculation unit 232, for example, based on various facial feature amounts such as the ratio between the distance between the eyes, the distance from the chin to the forehead, and the length from the chin to the nose, Each difference sum, average ratio, or ratio sum is obtained as the similarity.

  The similarity calculation result totaling unit 234 totals the calculation results of the similarity with the registered face image for each face image of the face image group stored in the similarity calculation result storage unit 233 to obtain a face image group. Is obtained and supplied to the similarity determination unit 235. More specifically, for example, the similarity calculation result accumulating unit 233 sets, for example, the similarity based on the face image having the highest similarity in the face image group as the similarity of the face image group, or The average value of the similarity between the registered face images and all the face images in the face image group is supplied to the buffer 235a of the similarity determination unit 235 as the similarity of the face image group.

  The similarity determination unit 235 sequentially accumulates the similarity supplied from the similarity calculation result totaling unit 234 in the buffer 235a, and the face that becomes the first among the similarities of the top three face image groups. The degree of similarity between the image group and a predetermined threshold is compared, and based on the comparison result, the first registered face image is similar to the face image group supplied from the face image determination unit 225. (Similarity indicating that the similarity is higher as the similarity is higher. If the similarity is higher than the predetermined threshold, or the similarity indicating that the similarity is lower as the similarity is lower, the predetermined threshold. Information of the face image having the highest similarity among the face image groups that are ranked in the top three are supplied to the display unit 23 and displayed to the communication unit 228. . Also, the similarity determination unit 235 compares the similarity with the face image group that is the first place and a predetermined threshold value, and the registered face image that is the first place is the face image determination unit 225 based on the comparison result. If the face image supplied is not similar, the face image having the highest similarity among the face image groups supplied from the face image determination unit 225 is supplied to the unregistered player database registration unit 227. To do.

  The unregistered player database registration unit 227 registers the face image, which has been supplied as being unregistered by the collation unit 226, in the unregistered player DB of the biometric information management database 3.

  The operation unit 229 includes buttons, a mouse, a keyboard, and the like. The operation unit 229 is operated when any one of the face images displayed on the display unit 23 with the above-mentioned similarity ranked in the top three is selected. Is supplied to the communication unit 228. The communication unit 228 includes an Ethernet board and the like, and distributes the selected face image to the mobile terminal 20 based on an operation signal from the operation unit 229.

  Here, for example, the similarity indicates a higher value as it is closer to the face image registered as a registered player as indicated by the ratio sum, and the similarity is a value higher than a predetermined threshold value. In this case, an example will be described in which it is determined that the face image of the registered player corresponding to the similarity is. However, for example, when the similarity is expressed as a difference sum in the respective feature amounts between the captured face image and the registered face image of the registered player, the similarity determination unit 235 uses the similarity as a threshold value. If it is smaller, the captured face image group is regarded as a registered player's face image, or in the case of an average ratio or the like, it is equal to or greater than a predetermined value in the range of 0 to 1. If the values are close, they can be regarded as the same person.

  When a new registered player database is distributed from the biometric information management center 2, the database management unit 230 updates the biometric information DB 22 based on the new registered player database.

[Registered player visit monitoring process]
Next, the registered player visit monitoring process will be described with reference to the flowchart of FIG.

  In step S1, the entrance camera 40 determines whether or not the door of the entrance is opened based on the information of the image being captured or the signal from the door of the entrance and exit until the predetermined time elapses. Repeat the process.

  If it is determined in step S <b> 1 that the door is opened, in step S <b> 2, the entrance camera 40 captures an image of the installed range and supplies the captured image to the image processing unit 39. The image acquisition unit 201 of the image processing unit 39 acquires the supplied image and supplies it to the face image extraction unit 202.

  In step S <b> 3, the face image extraction unit 202 extracts a player's face image from the supplied image and supplies the extracted image to the transmission unit 203. More specifically, the face image extraction unit 202 extracts a rectangular face image from the captured images by template matching using a reference template corresponding to the contour of the face image, and supplies it to the transmission unit 203. . The transmission unit 203 temporarily accumulates the supplied face image in the buffer 203a. The face image extraction unit 202 extracts a rectangular face image from the captured image, which is a portion where the skin is exposed due to a color scheme or the like, and is based on the arrangement of characteristic parts such as eyes and nose. It is also possible to detect vertices such as the head by template matching or chroma key processing based on the facial organs (eyes, nose, ears, etc.), and use a neural network to learn from the teacher signal and look like a face You may make it extract a face image by the method of detecting an area | region as a face image.

  In step S4, the face image extraction unit 202 determines whether there is a face image from the supplied image, that is, for example, the same person as the face image extracted in the process immediately before the image captured by the entrance camera 40 It is determined whether or not the facial image can be extracted. If it is determined that the facial image can be extracted, the process returns to step S2 and the processes of steps S2 to S4 are repeated. That is, as long as the face image can be continuously extracted, the processes in steps S2 to S4 are repeated. As described above, since the capacity of the buffer 203a is limited, it is determined that the face image cannot be extracted when the face image stored in the buffer 203a is full, that is, the face image does not exist. Alternatively, it may be determined that a face image cannot be extracted when a predetermined number of images are accumulated. In the following description, when six face images are extracted as the predetermined number, it is determined that the face image cannot be extracted.

  Therefore, in step S4, for example, when the processes in steps S2 to S4 have been repeated six times so far and face images have been extracted from the respective images, even if face images are present in the subsequent images. Even if it does not exist, it is considered that there is no face image, and the process proceeds to step S5.

  In step S <b> 5, the transmission unit 203 reads a plurality of face images from the buffer 203 a that stores the face images supplied from the face image extraction unit 202, and transmits them as a face image group to the biological information recognition apparatus 21. At this time, the transmission unit 203 adds information such as a camera ID for identifying each of the camera 38, the entrance camera 40, or the in-store camera 41 and information on transmission time to the face image group and transmits the information to the biological information recognition apparatus 21. To do.

  In step S21, the face image acquisition unit 221 of the biological information recognition apparatus 21 acquires a face image group. In step S <b> 22, the face image acquisition unit 221 extracts one unprocessed face image group from the supplied face image group and supplies the extracted face image group to the face image reliability calculation unit 222.

  In step S23, the face image reliability calculation unit 222 to the face organ reliability calculation unit 224 execute a reliability calculation process to calculate the face image reliability and the face organ reliability.

[Reliability calculation processing]
Here, the reliability calculation processing will be described with reference to the flowchart of FIG.

  In step S51, the face image reliability calculation unit 222 calculates the reliability of a plurality of face images included in the face image group supplied from the face image acquisition unit 221 as the face image reliability tr1, and the face image together with the face image This is supplied to the organ extraction unit 223. For example, when the face image reliability calculation unit 222 detects a face by template matching using a reference template corresponding to the face outline in the face image, the face image reliability calculation unit 222 calculates the ratio of the number of pixels that match between the detected face image and the reference template. Calculated as face detection reliability.

  In step S52, the facial organ extraction unit 223 extracts feature points constituting each organ from the regions constituting the facial image acquired by the facial image acquisition unit 221 as facial organs, and the facial organ, facial image reliability, The face image group is supplied to the facial organ reliability calculation unit 224. For example, the facial organ extraction unit 223 learns a pattern including the positions of pixels that are feature points among the pixels constituting the facial organ, and detects the feature points by matching using the learned data, or the acquired face Inside the image, feature points are detected by pattern matching.

  In step S53, the facial organ reliability calculation unit 224 calculates, as the facial organ reliability tr2, a value representing the reliability of the facial organ information composed of the pixel position information of the feature points extracted by the facial organ extraction unit 223. The face organ reliability, the face image reliability, and the face image group are supplied to the face image determination unit 225. More specifically, when using a method of detecting feature points by pattern matching, the facial organ reliability calculation unit 224 uses the luminance value vector of each feature point learned in advance and the vicinity of the feature point extracted as the facial organ. The Euclidean distance from the luminance value vector obtained from the region is calculated as the facial organ reliability.

  Through the above processing, a face image is acquired, and the face image reliability tr1 and the face organ reliability tr2 are obtained and supplied to the face image determination unit 225.

  Now, the description returns to the flowchart of FIG.

  In step S24, the face image determination unit 225 determines whether the supplied face image is a falsely detected face image based on the face image reliability tr1 and the face organ reliability tr2. . More specifically, when the face image reliability tr1 is lower than a predetermined threshold or the face organ reliability tr2 is lower than a predetermined threshold, the face image determination unit 225 has a reliability as a face image. It is determined that the detection error is low. Therefore, in step S24, for example, when the face image is a face image of a character drawn on a poster or the like and the face image reliability is low or the face organ reliability is low, it is regarded as a false detection. The process proceeds to step S38.

  In step S38, the face image determination unit 225 discards the supplied face image group, and the process proceeds to step S36.

  On the other hand, when the face image of the correct person is acquired in step S24, the face image reliability is higher than a predetermined threshold value, and the facial organ reliability is higher than the predetermined threshold value, the face image determination unit 225 The processing proceeds to step S25, assuming that it is not a false detection.

  In step S <b> 25, the face image determination unit 225 supplies the acquired face image to the collation unit 226.

  In other words, the face image determination unit 225 is not supplied to the subsequent collation unit 226 other than those that can be trusted as face images based on the face image reliability and the face organ reliability. With such a process, since the matching process in the matching unit 226 is not performed for a face image that has low reliability in the first place, it is possible to suppress erroneous detection.

  In step S26, the feature amount extraction unit 231 of the collation unit 226 extracts one unprocessed face image from the supplied face image group. In step S27, the feature amount extraction unit 231 extracts the feature amount, Also supplied to the similarity calculator 232.

  In step 28, the similarity calculation unit 232 executes similarity calculation processing.

  Here, the similarity calculation processing will be described with reference to the flowchart of FIG.

  In step S71, the similarity calculation unit 232 extracts a face image of one unprocessed registered player from the face images registered in the biometric information DB 22, and sets it as a processing target.

  In step S <b> 72, the similarity calculation unit 232 extracts a feature amount similar to the feature amount supplied from the feature amount extraction unit 231 from the face image registered in the biological information DB 22 set as a processing target.

  In step S73, the similarity calculation unit 232 determines the ratio of the eye-to-eye spacing, the length from the chin to the forehead, and the length from the chin to the nose for the face image supplied from the feature amount extraction unit 231. The difference sum, the average ratio, or the ratio sum is calculated as the degree of similarity using the various face feature amounts and the similar feature amounts in the face image registered in the biological information DB 22, and step S74. Then, the similarity with the face image registered in the biological information DB 22 as the calculation result is supplied to the similarity determination unit 235 and registered in the buffer 233a.

  In step S75, the similarity calculation unit 232 determines whether or not an unprocessed registered player's face image exists in the biometric information DB 22. If there is an unprocessed registered player's face image, the process is performed. Return to step S71. That is, the processes in steps S71 to S75 are repeated until the similarity with the face images of all registered players in the biometric information DB 22 is calculated. If it is determined in step S75 that the unprocessed registered player's face image does not exist in the biometric information DB 22, the similarity calculation process ends.

  Now, the description returns to the flowchart of FIG.

  In step S29, the similarity determination unit 235 obtains the rank based on the calculation result of the similarity registered in the buffer 233a, and the face images up to the top three of the face images registered in the biometric information DB 22 are obtained. And similarity information are extracted. In step S30, the similarity calculation unit 232 determines whether there is an unprocessed face image in the selected face image group. If there is an unprocessed face image, the process proceeds to step S30. Return to S26. That is, the processes in steps S26 to S30 are repeated until there is no unprocessed face image in the selected face image group.

  If it is determined in step S30 that there is no unprocessed face image, that is, for all the face images in the selected face image group, the similarity to the registered player's face image is obtained, in step S31. The similarity calculation result totaling unit 234 totals the similarity calculation results for each face image accumulated in the similarity calculation result accumulation unit 233 and registers the aggregation results in the buffer 235 a of the similarity determination unit 235.

  In step S <b> 32, the similarity determination unit 235 obtains information about the similarity between the face images that are candidates for the top three ranks as the face image group supplied from the similarity calculation result totaling unit 234 registered in the buffer 235 a. Based on the above, it is determined whether or not the highest degree of similarity is greater than a predetermined threshold. That is, the similarity determination unit 235 registers the most similar registered player (the most similar face image group acquired by the face image acquisition unit 221 among the face images registered in the biometric information DB 22). Player: Here, the similarity of the registered player having the highest similarity is compared with a predetermined threshold.

  Note that, as described above, the similarity between the face image of the registered player who is most similar to the captured face image is not necessarily the highest because of the definition of the similarity. And the threshold value may differ from those in this example.

  If it is determined in step S32 that the highest similarity is greater than the predetermined threshold, in step S33, the similarity determination unit 235 determines that the top third facial image supplied from the similarity calculation unit 232 is the top three. A notification screen indicating that the face image is a candidate for a registered player is displayed by controlling the display unit 23.

  In step S34, the communication unit 228 determines whether or not any of the candidate face images has been selected by operating the operation unit 229.

  In step S34, for example, when any of the candidate face images is selected by operating the operation unit 229, it is considered that the candidate face image is selected. In step S32, the communication unit 228 selects The candidate face image and the camera image captured by the entrance camera 40 are transmitted to the mobile terminal 20 to notify that the corresponding registered player has visited the store.

  By the above processing, the staff in the game store 1 can recognize the registered player's visit if the portable terminal 20 is possessed.

  In step S36, the face image acquisition unit 221 determines whether or not all of the supplied face images have been processed. If there is an unprocessed face image, the process returns to step S22. That is, the processes in steps S22 to S36 are repeated until the process is performed on all face images. If it is determined that the process has been completed for all face images, the process returns to step S21.

  On the other hand, if no candidate face image is selected in step S34, or based on the top three face images and similarity information supplied from the similarity calculation unit 232 in step S32, If the highest similarity is not greater than a predetermined threshold, that is, if the similarity is less than the predetermined threshold even for the most similar registered player's face image, the similarity is determined in step S37. The determination unit 235 supplies the face image having the highest similarity in the face image group supplied from the image processing unit 39 to the unregistered player database registration unit 227. The unregistered player database registration unit 227 accesses the biometric information management database 3 through the biometric management information bus 6 and the public communication line network 8 to register the supplied face image. Note that the face image supplied to the unregistered player database registration unit 227 may be any face image in the face image group, and may not necessarily be the face image having the highest similarity.

  With the above processing, the similarity is obtained using a plurality of face images from the face image group supplied from the image processing unit 39 by the biometric information recognition device 21, so that the face image is recognized more accurately. It becomes possible to do. When the face image group supplied from the image processing unit 39 is regarded as a face image group of face images not registered in the biometric information DB 22, the biometric information management managed by the biometric information management center 2 is performed. It is registered in DB3 as a face image of an unregistered player.

  Further, a face image that is regarded as having low reliability based on the face image reliability and the facial organ reliability indicating the reliability of the face image is not supplied to the matching unit 226. Since only the face image with high reliability is supplied to the collation unit 226, it is possible to prevent erroneous detection even if the face area of a character such as a poster or a doll is extracted as a face image.

  Incidentally, the series of monitoring processes described above can be executed by hardware, but can also be executed by software. When a series of processing is executed by software, a program constituting the software may execute various functions by installing a computer incorporated in dedicated hardware or various programs. For example, it is installed from a recording medium in a general-purpose personal computer or the like.

  FIG. 14 shows a configuration example of a general-purpose personal computer. This personal computer incorporates a CPU (Central Processing Unit) 1001. An input / output interface 1005 is connected to the CPU 1001 via the bus 1004. A ROM (Read Only Memory) 302 and a RAM (Random Access Memory) 1003 are connected to the bus 1004.

  An input / output interface 1005 includes an input unit 1006 including an input device such as a keyboard and a mouse for a user to input an operation command, an output unit 1007 for outputting a processing operation screen and an image of a processing result to a display device, a program and various data A storage unit 1008 including a hard disk drive for storing data, a LAN (Local Area Network) adapter, and the like, and a communication unit 1009 for performing communication processing via a network represented by the Internet are connected. Also, a magnetic disk (including a flexible disk), an optical disk (including a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versatile Disc)), a magneto-optical disk (including an MD (Mini Disc)), or a semiconductor A drive 1010 for reading / writing data from / to a removable medium 1011 such as a memory is connected.

  The CPU 1001 is read from a program stored in the ROM 1002 or a removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, installed in the storage unit 1008, and loaded from the storage unit 1008 to the RAM 303. Various processes are executed according to the program. The RAM 1003 also appropriately stores data necessary for the CPU 1001 to execute various processes.

  In this specification, the step of describing the program recorded on the recording medium is not limited to the processing performed in time series in the order described, but of course, it is not necessarily performed in time series. Or the process performed separately is included.

  Further, in this specification, the system represents the entire apparatus constituted by a plurality of apparatuses.

1, 1-1 to 1-n amusement store 2 biometric information management center 3 biometric information management database 4 third party amusement store management center 5 third party amusement store management database 6 biometric information management bus 7 third party amusement store management bus 8, 9 Public communication network 21 Biometric information recognition device 22 Biometric information database 24 Game store management device 26 Game table management database 27 Medium lending management device 29 Medium lending management database 30 Amusement store management information bus 31 Biometric information bus 33 Settling machine 34 Lending machine 35 Counter 36,36-1 thru 36-m Game stand 37,37-1 thru 37-m Game stand peripheral terminal 38,38-1 thru 38-m Camera 39,39-1 thru 39- (m + p + q) Image processing unit 40, 40-1 to 40-p Entrance camera 41, 41-1 to 41-q In-store camera

Claims (8)

Acquisition means for acquiring a face image of the person to be verified from the image;
Collating means for calculating and collating the similarity between the face image of the person to be collated acquired by the obtaining means and the face image of the accumulator accumulated in the accumulating means for accumulating the face image as the face image of the accumulator ; ,
A similarity determination unit that determines whether or not the face image of the person to be collated is the face image of the accumulator by comparing the similarity that is the collation result of the collation unit and a predetermined threshold;
Face image reliability calculation means for calculating the reliability of the face image acquired by the acquisition means as the face image reliability;
Organ extraction means for extracting a facial organ from the face image acquired by the acquisition means;
A vector of luminance values of pixels at pixel positions that are pattern of feature points composing the organ in the face image obtained by learning by a teacher signal, and a pattern of feature points composing the organ extracted by the organ extraction means An organ reliability calculation means for calculating a distance in the Euclidean space from a vector of luminance values of the pixel at the pixel position as an organ reliability;
The face image reliability, and none of the organ reliability is higher than a predetermined threshold, the verification unit, and the similarity determination unit, and a reliability determination unit configured to control so as to execute the process Including information processing apparatus. The method of acquiring a face image from the image by the acquiring means includes template matching using a template corresponding to the contour shape of the face, template matching based on an organ constituting the face, and a head that forms a face by chroma key processing. After detecting the vertices, a method of detecting a face image based on the vertices, a method of detecting based on the shape of the skin color region, or a method of detecting a face-like region by learning with a teacher signal based on a neural network The information processing apparatus according to claim 1. The face image reliability calculation means calculates a value indicating the degree of matching with the template as the face image reliability by template matching using a template corresponding to the face outline based on the face image. The information processing apparatus according to claim 1 or 2 . The organ extracting means extracts information on pixel positions constituting the organ as feature points based on the face image, and extracts feature points composed of information on pixel positions constituting the organ including the outline of the face image. Extracting by pattern matching using a pattern obtained by learning the arrangement, or pattern matching based on an arrangement pattern of feature points consisting of information on pixel positions constituting the organ in the face image region The information processing apparatus according to any one of claims 1 to 3 . The information processing apparatus further comprises notification means for notifying that the collation target person is the accumulator when the similarity determination means determines that the collation target person's face image is the accumulator face image. The information processing apparatus according to any one of claims 1 to 4 . A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 5. Acquisition means, and acquiring the collation object person of the face image from the image,
A face image reliability calculating unit that calculates the reliability of the face image acquired by the process of the acquiring step as the face image reliability;
An organ extracting step for extracting an organ of the face from the face image acquired by the processing of the acquiring step;
The organ reliability calculation means calculates the brightness value vector of the pixel at the pixel position that is the pattern of the feature points constituting the organ in the face image obtained by learning with the teacher signal, and the organ extracted by the organ extraction means. An organ reliability calculation step for calculating a distance in the Euclidean space with a vector of luminance values of the pixels at the pixel positions that become the pattern of feature points constituting the organ reliability,
A determination step for determining whether or not each of the face image reliability and the organ reliability is higher than a predetermined threshold;
The process of the determination step, the face image reliability, and none of the organ reliability, when it is determined to be higher than a predetermined threshold, collation means, verification object obtained by the processing of the acquisition step A collation step for calculating and collating the similarity between the person's face image and the accumulator's face image accumulated in the accumulating means for accumulating the face image as the accumulator 's face image;
Determining class similarity score determination unit, by comparing the collation result is a similarity with a predetermined threshold in the processing of the verification step, whether or not the face image of the collation object person is a face image of the storage's A similarity determination step,
An information processing method including: When the notification means determines that the face image of the person to be collated is the face image of the accumulator by the processing of the similarity determination step, a notifying step for notifying that the person to be collated is the accumulator The information processing method according to claim 7, further comprising:

Images