WO2021250845A1 - 認証方法、情報処理装置、及び認証プログラム - Google Patents

認証方法、情報処理装置、及び認証プログラム Download PDF

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Publication number
WO2021250845A1
WO2021250845A1 PCT/JP2020/023005 JP2020023005W WO2021250845A1 WO 2021250845 A1 WO2021250845 A1 WO 2021250845A1 JP 2020023005 W JP2020023005 W JP 2020023005W WO 2021250845 A1 WO2021250845 A1 WO 2021250845A1
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Prior art keywords
persons
biometric information
authentication
person
sensor
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English (en)
French (fr)
Japanese (ja)
Inventor
壮一 ▲浜▼
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Fujitsu Ltd
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Fujitsu Ltd
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Priority to EP20939653.0A priority Critical patent/EP4167112A4/en
Priority to PCT/JP2020/023005 priority patent/WO2021250845A1/ja
Priority to JP2022530453A priority patent/JPWO2021250845A1/ja
Priority to CN202080101104.5A priority patent/CN115668186A/zh
Publication of WO2021250845A1 publication Critical patent/WO2021250845A1/ja
Priority to US17/981,518 priority patent/US20230059121A1/en
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns
    • G06V40/145—Sensors therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2117—User registration
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition

Definitions

  • the present invention relates to authentication technology.
  • Biometric authentication is a technology that uses biometric features such as fingerprints, palm prints, veins, and faces to verify identity.
  • biometric authentication the biometric features acquired from the person to be authenticated are compared (verified) with the biometric features registered in advance in the registration template, and based on the comparison result indicating whether or not the biometric features match. Authentication is performed for the person to be authenticated.
  • the biological features registered in the registration template are sometimes called registration data.
  • Biometric authentication is used in various fields such as bank ATMs (Automated Teller Machines) and room entry / exit management, and in recent years, it has begun to be used for cashless payments in stores such as supermarkets and convenience stores.
  • bank ATMs Automatic Teller Machines
  • room entry / exit management and in recent years, it has begun to be used for cashless payments in stores such as supermarkets and convenience stores.
  • 1: 1 authentication and 1: N authentication are known as authentication methods for biometric authentication.
  • 1: 1 authentication is an authentication method that compares the biological characteristics of the person to be authenticated with the registered data specified by an ID, card, etc. such as a PIN (Personal Identification Number) code.
  • N authentication is an authentication method that searches a plurality of registered data for registered data that matches the biological characteristics of the person to be authenticated. In stores and the like, 1: N certification is often adopted from the viewpoint of convenience.
  • the registered data is narrowed down by a simple PIN code or the like, the set of registered data to be searched is made sufficiently small, and then 1: N authentication is performed. How small the set of registered data should be to a practical level depends on the type of biological characteristics. However, even if it is a simple PIN code, forcing the authentication target person to input the PIN code impairs convenience.
  • an authentication method has been proposed in which a set of registered data is narrowed down by one biological feature and an authentication target person is authenticated by another biological feature by using a plurality of types of biological features. Since convenience is impaired if multiple types of biological features are acquired individually, an authentication method that acquires the palm vein at the same time as a fingerprint, an authentication method that captures a facial image when the palm vein is acquired, and the like have been proposed (for example).
  • Patent Document 1 and Non-Patent Document 1 Non-Patent Document 1.
  • Non-Patent Document 2 A technique for estimating the two-dimensional postures of a plurality of people in an image is also known (see, for example, Non-Patent Document 2).
  • Non-Patent Document 1 when the biometric authentication technique described in Non-Patent Document 1 is used to narrow down a set of registered data with a face image and authenticate a person to be authenticated with a palm vein, the load of the authentication process may increase.
  • the present invention aims to reduce the load of authentication processing in biometric authentication using a face image and biometric information other than the face image.
  • the computer receives the authentication target biometric information detected by the first sensor.
  • the computer identifies any person included in the one or more persons based on the movement of each of the one or more persons.
  • the computer selects the registered biometric information associated with the registered facial image information similar to the facial image information of any of the specified persons from the registered biometric information associated with each of the plurality of registered facial image information. do.
  • the computer authenticates the authentication target biometric information based on the comparison result of comparing the authentication target biometric information with the selected registered biometric information.
  • the load of the authentication process can be reduced.
  • a biometric authentication system that narrows down a set of registered data with a face image and authenticates a person to be authenticated with a palm vein will be examined.
  • this biometric authentication system for example, by performing face authentication, a list of N candidates (N is an integer of 1 or more) for the authentication target person is generated. Then, by performing 1: N authentication using the registration data of the palm vein of each candidate included in the generated list, the authentication process for the authentication target person is performed.
  • multiple faces may be photographed at the same time depending on the installation status of the camera that captures the face image or the usage status of the user who is the authentication target. For example, when the face images of three people are acquired, the list for three people is generated, so that the target person of the palm vein authentication is 3N people, and the processing time of the palm vein authentication is acquired by one person's face image. It will be 3 times as much as if it was done. Further, when the initially set N is the upper limit of 1: N authentication using the palm vein, the risk of accepting another person who erroneously authenticates another person as the person increases.
  • a method of selecting a user's face image based on the size or position of each face image from a plurality of face images included in the captured image can be considered.
  • a person with a larger face image is closer to the camera and is more likely to be a user. Further, the closer the position of the face image is to a specific position such as the center of the captured image, the higher the possibility of being a user.
  • the face image of the user and the face image of the companion in the captured image may be almost the same size. Further, when the user tries to stand in front of the terminal device side by side with the companion, the position of the user's face image may be deviated from the center. Therefore, when selecting a user's face image based on the size or position of each face image, it is difficult to set an appropriate selection criterion.
  • FIG. 1 shows an example of a functional configuration of an information processing device (computer) of an embodiment.
  • the information processing device 101 of FIG. 1 includes a reception unit 111, a specific unit 112, a selection unit 113, and an authentication unit 114.
  • FIG. 2 is a flowchart showing an example of biometric authentication processing performed by the information processing apparatus 101 of FIG.
  • the reception unit 111 receives the authentication target biometric information detected by the first sensor (step 201).
  • the identification unit 112 identifies any person included in one or more persons based on the movement of each of the one or more persons. (Step 202).
  • the selection unit 113 is associated with the registered face image information similar to the face image information of any of the specified persons from the registered biometric information associated with each of the plurality of registered face image information. Select the registered biometric information (step 203). Then, the authentication unit 114 authenticates the authentication target biometric information based on the comparison result of comparing the authentication target biometric information with the selected registered biometric information (step 204).
  • the load of the authentication process can be reduced in the biometric authentication using the face image and the biometric information other than the face image.
  • FIG. 3 shows a specific example of the information processing apparatus 101 of FIG.
  • the information processing device 301 of FIG. 3 includes a storage unit 311, a biometric information acquisition unit 312, a video acquisition unit 313, a person detection unit 314, a stillness determination unit 315, a face selection unit 316, a face authentication unit 317, and a biometric information selection unit 318. It includes a biometric authentication unit 319 and an output unit 320.
  • the information processing device 301 may be, for example, a server included in a financial processing system of a financial institution, an entry / exit management system, or a payment system of a retail store.
  • the biometric information acquisition unit 312, the biometric information selection unit 318, and the biometric authentication unit 319 correspond to the reception unit 111, the selection unit 113, and the authentication unit 114 in FIG. 1, respectively.
  • the stationary determination unit 315 and the face selection unit 316 correspond to the specific unit 112 in FIG.
  • the biological sensor 302 is an example of the first sensor, and the image pickup apparatus 303 is an example of the second sensor.
  • the biological sensor 302 is, for example, a vein sensor, a fingerprint sensor, an image sensor (camera), or the like, and photographs a living body such as a palm or a finger to acquire a biological image such as a vein image, a fingerprint image, or a palm print image.
  • the biosensor 302 is a vein sensor
  • the biosensor 302 irradiates the palm with near infrared rays or the like to photograph blood vessels or the like inside the hand.
  • the biosensor 302 outputs the acquired bioimage information to the information processing device 101 as authentication target biometric information 333.
  • the authentication target biological information 333 may be a biological image or a pattern generated from the biological image.
  • the patterns generated from the biological image are a vein pattern, a fingerprint pattern, a palm print pattern, and the like.
  • the image pickup device 303 is a camera having an image pickup element such as a CCD (Charge-Coupled Device) or a CMOS (Complementary Metal-Oxide-Semiconductor), and captures an image 334 of a person to be certified.
  • the image 334 taken by the image pickup apparatus 303 includes a plurality of time-series images. The image at each time is an example of a photographed image. The image at each time is sometimes called a frame.
  • the image pickup apparatus 303 outputs the captured image 334 to the information processing apparatus 301.
  • the biometric information acquisition unit 312 receives the authentication target biometric information 333 by acquiring the authentication target biometric information 333 from the biometric sensor 302. Then, the biometric information acquisition unit 312 stores the authentication target biometric information 333 in the storage unit 311 together with the time information indicating the reception time of the authentication target biometric information 333.
  • the image acquisition unit 313 acquires the image 334 from the image pickup device 303, receives the image 334, and stores the image 334 in the storage unit 311.
  • the storage unit 311 stores the registered biometric information 331 and the registered facial image information 332 of each of the plurality of registrants.
  • the registered biometric information 331 of each person includes the user ID and biometric information of the person.
  • the biometric information may be a biometric image or a pattern generated from the biometric image.
  • the registered face image information 332 of each person includes the user ID and face image information of the person.
  • the face image information may be a face image or a feature amount indicating the features of the face image.
  • As the feature amount of the face image for example, HOG (Histograms of Oriented Gradients) feature amount, SIFT (Scaled Invariance Feature Transform) feature amount, SURF (Speeded-Up Robust Features) feature amount and the like can be used.
  • the feature amount of the face image may be a BRIEF (Binary Robust Independent Elementary Features) feature amount or a saliency.
  • the biometric information included in the registered biometric information 331 of each person and the facial image information included in the registered facial image information 332 of each person are associated with each other via the user ID.
  • a plurality of persons including the authentication target person may appear in the image 334 at the same time.
  • the authentication target person inputs a biometric image to the biometric sensor 302
  • the authentication target person performs an operation of holding his / her hand over the biometric sensor 302.
  • the standing position or the position of the head is significantly changed, the movement of holding the hand is hindered, so that the body parts other than the hand of the person to be authenticated are often in a substantially stationary state.
  • FIG. 4 shows an example of a photographed image in which a plurality of people are shown.
  • the captured image of FIG. 4 includes a face image 401 to a face image 403.
  • the face image 403 corresponds to the face image of the authentication target person
  • the face image 401 and the face image 402 correspond to the face image of a person other than the authentication target person.
  • the person to be authenticated stands still in front of the biosensor 302 in order to hold the hand 411 over the biosensor 302, and the face image 403 in the image 334 also stands still.
  • the face image 401 and the face image 402 in the image 334 also continue to move.
  • the set of registered biometric information 331 to be compared with the authentication target biometric information 333 can be narrowed down from the registered biometric information 331 of a large number of registrants.
  • the total number of registrants is about 1 million, and the number of registrants after narrowing down is about 10,000.
  • the image pickup device 303 starts shooting the image 334 when the biosensor 302 is in the input waiting state.
  • the image pickup apparatus 303 may detect an approaching person by using a proximity sensor (not shown) or the like, and may start shooting when the person is detected. Further, the image pickup apparatus 303 may always stand by in the shooting state.
  • the person detection unit 314 detects a person from each image included in the video 334, and assigns a person ID to the detected person. Then, the person detection unit 314 stores the position information 335 indicating the position of each person in the image in the storage unit 311 in association with the person ID.
  • the position indicated by the position information 335 may be the position of a specific body part such as the head, face, or neck of the person shown in the image.
  • the person detection unit 314 can specify the position of a specific body part by detecting the skeleton information of the body from the image by using, for example, the deep learning described in Non-Patent Document 2.
  • the specific body part is the head or face
  • the coordinates of the bounding box of the head or face may be used as the position information 335.
  • the person detection unit 314 further detects a face image showing the face of each person from each image included in the video 334, and assigns the person ID of the person to the detected face image.
  • a face image showing the face of each person from each image included in the video 334
  • assigns the person ID of the person to the detected face image In the example of FIG. 4, "A”, "B”, and “C” are assigned as the person IDs of the face image 401, the face image 402, and the face image 403, respectively.
  • the person detection unit 314 assigns the same person ID to the same person detected from different images by tracking an object among a plurality of images included in the video 334. As a result, the same person is associated with each other among the plurality of images.
  • the person detection unit 314 may consider a person whose change amount of the position of a specific body part is less than a predetermined value among a plurality of images as the same person. Further, the person detection unit 314 may calculate a face correlation value between a plurality of images by using pattern matching or the like, and may consider a person whose calculated correlation value is larger than a predetermined value as the same person. ..
  • the rest determination unit 315 determines whether or not the body part of each person is stationary by using the position information 335. Then, the rest determination unit 315 identifies a person having a body part stationary at the time of detecting the authentication target biometric information 333 as an authentication target person, and outputs the person ID of the specified person to the face selection unit 316. At this time, the stationary determination unit 315 uses the reception time indicated by the time information of the authentication target biological information 333 as the time when the authentication target biological information 333 is detected.
  • FIG. 5 shows an example of the first static determination for the three persons shown in FIG.
  • the horizontal axis represents the time t
  • the vertical axis represents the position coordinates x of the body parts of each person in the captured image.
  • the position coordinates x may be the horizontal coordinates of the captured image or the vertical coordinates of the captured image.
  • Curves 501 to 503 represent position information 335 of person ID "A” to person ID “C”, respectively.
  • the curve 501 represents a time change of the position coordinate x of the person having the person ID “A”
  • the curve 502 represents the time change of the position coordinate x of the person having the person ID “B”
  • the curve 503 represents the time change of the position coordinate x of the person having the person ID “B”. It represents the time change of the position coordinate x of the person having "C”.
  • the person having the person ID "C" corresponds to the person to be authenticated.
  • Time t1 represents the reception time indicated by the time information of the biometric information 333 to be authenticated, and ⁇ t represents the time interval between two consecutive images. Therefore, the time t1- ⁇ t represents the shooting time of the image taken immediately before the image at the time t1. ⁇ t is obtained by the following equation using the frame rate fr of the image pickup apparatus 303.
  • the static determination unit 315 determines the amount of change ⁇ x of the position coordinates x of the person between the time t1- ⁇ t and the time t1 by the following equation. calculate.
  • ⁇ x represents the amount of change on the image, and the amount of movement in the three-dimensional space corresponding to ⁇ x changes according to the distance between the image pickup device 303 and the person. Even if the amount of movement in the three-dimensional space is the same, the ⁇ x of the person far from the image pickup device 303 is small, and the ⁇ x of the person close to the image pickup device 303 is large.
  • the rest determination unit 315 determines the threshold value TH used for the rest determination by using, for example, the landmark coordinates of the face image of each person. For example, when the position coordinates of the right eye in the face image are x (eye1) and the position coordinates of the left eye are x (eyee2), the distance w between the right eye and the left eye is given by the following equation.
  • the stationary determination unit 315 can set the threshold value TH by the following equation using the distance w.
  • K is a positive integer. k may be an integer in the range of 5 to 15.
  • the rest determination unit 315 determines that the person is stationary at time t1 when ⁇ x is smaller than TH, and determines that the person is not stationary at time t1 when ⁇ x is TH or more. In the example of FIG. 5, it is determined that the person having the person ID "C" is stationary, and the person having the person ID "A” and the person having the person ID "B" are determined not to be stationary. To.
  • the static determination shown in FIG. 5 uses only the position coordinates x at the two times of time t1- ⁇ t and time t1, there is a possibility that an erroneous determination may occur due to the influence of noise in the image. Therefore, the influence of noise can be reduced by giving a certain width to the time range of the static determination and obtaining the statistical value of the change amount of the position coordinates x in the time range.
  • the statistical value an average value, a median value, a mode value, or the like can be used.
  • the movement of the authentication target person may be stopped in order to confirm the biometric information input instruction displayed on the screen.
  • it is effective to use the output time of the biometric information input instruction as the start time of the time range.
  • FIG. 6 shows an example of a second static determination for the three persons shown in FIG.
  • the time t0 represents the output time of the biometric information input instruction.
  • the time range of the static determination is the range from the time t0 to the time t1.
  • the stationary determination unit 315 calculates the average change amount ⁇ x_ave of the position coordinates x in this time range by the following equation.
  • N1 represents the number of images in the range from time t0 to time t1 and is given by the following equation.
  • n1 (t1-t0) / ⁇ t + 1 (6)
  • the rest determination unit 315 determines that the person is stationary at time t1 when ⁇ x_ave is smaller than TH, and determines that the person is not stationary at time t1 when ⁇ x_ave is TH or more.
  • the time range for static determination can be set by using the fixed length time ⁇ instead of the time t0.
  • FIG. 7 shows an example of a third static determination for the three persons shown in FIG.
  • the time range of the static determination is the range from the time t1- ⁇ to the time t1.
  • the stationary determination unit 315 calculates the average change amount ⁇ x_ave of the position coordinates x in this time range by the following equation.
  • N2 represents the number of images in the range from time t1- ⁇ to time t1 and is given by the following equation.
  • n2 ⁇ / ⁇ t + 1 (8)
  • ⁇ may be 5 to 15 times the time of ⁇ t.
  • FIG. 8 shows an example of a fourth static determination for the three persons shown in FIG.
  • the time range of the static determination is the range from the time t1- ⁇ 1 to the time t1 + ⁇ 2.
  • the stationary determination unit 315 calculates the average change amount ⁇ x_ave of the position coordinates x in this time range by the following equation.
  • N3 represents the number of images in the range from time t1- ⁇ 1 to time t1 + ⁇ 2, and is given by the following equation.
  • n3 ( ⁇ 1 + ⁇ 2) / ⁇ t + 1 (10)
  • ⁇ 1 may be 5 to 15 times as long as ⁇ t, and ⁇ 2 may be shorter than ⁇ 1.
  • the input authentication target biological information 333 of the person is determined by determining whether or not the body part is stationary by using the position coordinates x of the body part of the person. It is possible to estimate whether or not it is biometric information. Further, by performing the rest determination using the statistical value of the amount of change in the position coordinates x in a predetermined time range, it is possible to improve the estimation accuracy of the person corresponding to the authentication target biometric information 333.
  • the biological sensor 302 When a contact type sensor such as a fingerprint sensor is used as the biological sensor 302, the movement of the person to be authenticated is stopped for a longer time than when a non-contact type sensor such as a vein sensor is used. Judgment accuracy is improved.
  • a contact type sensor such as a fingerprint sensor
  • a non-contact type sensor such as a vein sensor
  • the face selection unit 316 selects a face image 336 corresponding to the person ID output from the static determination unit 315 from a plurality of face images included in the video 334, and stores the face image 336 in the storage unit 311.
  • the face recognition unit 317 performs face recognition on the face image 336 by comparing the face image 336 with each registered face image information 332.
  • the face recognition unit 317 calculates, for example, the degree of similarity between the face image 336 and each registered face image information 332.
  • the face authentication unit 317 uses the feature amount F1 of the face image 336 and the feature amount of the face image included in the registered face image information 332. F2 is calculated, and the similarity is calculated using the feature amount F1 and the feature amount F2.
  • the face recognition unit 317 calculates the feature amount F1 of the face image 336 and uses the feature amount F1 and the feature amount F2 to determine the similarity. To calculate.
  • the biometric information selection unit 318 selects a predetermined number of registered face image information 332s in descending order of similarity calculated by the face recognition unit 317. Then, the biological information selection unit 318 generates a candidate list 337 including the user ID of the selected registered face image information 332 and stores it in the storage unit 311. The biometric information selection unit 318 selects the registered biometric information 331 corresponding to each user ID in the candidate list 337 by generating the candidate list 337. Thereby, the set of the registered biometric information 331 to be compared with the authentication target biometric information 333 can be narrowed down from the registered biometric information 331 of the plurality of persons.
  • the biometric authentication unit 319 performs biometric authentication for the authentication target biometric information 333 by comparing the authentication target biometric information 333 with the registered biometric information 331 corresponding to each user ID in the candidate list 337. Then, the biometric authentication unit 319 generates the authentication result 338 and stores it in the storage unit 311.
  • the biometric authentication unit 319 calculates, for example, the similarity between the biometric information 333 to be authenticated and each registered biometric information 331, and stores the user ID of the registered biometric information 331 having the highest similarity as the authentication result 338. Store in 311.
  • the output unit 320 outputs the authentication result 338.
  • the information processing device 301 of FIG. 3 even when a plurality of persons are shown in the video 334, it is possible to identify a person who is likely to be an authentication target person. By generating the candidate list 337 based on the face image of the specified person, the set of registered biometric information 331 is appropriately narrowed down.
  • the load of the process of detecting a person from each image, the static determination process using the position information 335 of each person, and the narrowing process of the registered biometric information 331 by the face image 336 is the biometric authentication using the biometric information 333 to be authenticated. Less than the processing load. Therefore, the load of biometric authentication on the biometric information 333 to be authenticated is reduced, and high-speed and highly accurate biometric authentication processing is realized.
  • the face image of a person other than the person to be authenticated is excluded from the processing target of face recognition, the privacy of the photographed person can be appropriately protected.
  • the movement of a person other than the authentication target person shown in the image 334 may accidentally stop in synchronization with the input of the biometric information by the authentication target person, and it may be determined that a plurality of people are stationary. obtain.
  • the static determination unit 315 may specify the person with the smallest amount of movement in the three-dimensional space as the person to be authenticated.
  • the information processing device 301 may try to identify the person to be authenticated by applying another determination criterion.
  • another determination criterion for example, the size or position of each face image in the image can be used. The larger the size of the face image, the closer to the image pickup device 303, and the higher the possibility that the person is the authentication target. Further, the closer the position of the face image is to the center of the image, the higher the possibility that the person is the authentication target.
  • the size or position of the face image alone is not sufficient as a judgment criterion, but it is effective to use it as auxiliary information for judgment.
  • the information processing apparatus 301 may generate a candidate list 337 by using each of a plurality of persons determined to be stationary as candidates for the authentication target person.
  • FIG. 9 is a flowchart showing a specific example of the biometric authentication process performed by the information processing device 301 of FIG.
  • the image pickup device 303 starts shooting the image 334 at the same time as the biometric authentication process is started, and the image acquisition unit 313 acquires the image 334 from the image pickup device 303.
  • the person detection unit 314 detects a person's face image from each image included in the video 334, and assigns a person ID to the detected face image (step 901).
  • the person detection unit 314 detects a position in the image of each person (step 902), and generates position information 335 indicating the position of each person (step 903).
  • the biometric information acquisition unit 312 instructs the authentication target person to input biometric information (step 904).
  • the biosensor 302 inputs the authentication target biometric information 333
  • the biometric information acquisition unit 312 acquires the authentication target biometric information 333 from the biosensor 302 (step 905).
  • the biometric information acquisition unit 312 acquires the time when the authentication target biometric information 333 is acquired as the reception time (step 906).
  • the rest determination unit 315 determines whether or not the body part of each person is stationary by using the position information 335, and the person who is stationary at the reception time of the authentication target biometric information 333 is the authentication target. Identify as a person (step 907). Then, the stillness determination unit 315 outputs the person ID of the specified person to the face selection unit 316, and the face selection unit 316 has the face corresponding to the person ID from the plurality of face images included in the video 334. Image 336 is selected (step 908).
  • the face recognition unit 317 performs face recognition on the face image 336, and the biometric information selection unit 318 generates a candidate list 337 based on the result of the face recognition (step 909).
  • the biometric authentication unit 319 performs biometric authentication on the authentication target biometric information 333 using the candidate list 337, and the output unit 320 outputs the authentication result 338 (step 910).
  • FIG. 10 is a flowchart showing an example of a biometric authentication process that omits the person identification process when only the authentication target person is shown.
  • the person detection unit 314 detects a person's face image from each image included in the video 334, and assigns a person ID to the detected face image (step 1001).
  • the biological information acquisition unit 312 performs the same process as in steps 904 to 906 of FIG.
  • the person detection unit 314 checks whether or not the detected face image is only the face image of one person (step 1002).
  • the information processing apparatus 301 performs the processes of steps 1004 and 1005.
  • the processing of step 1004 and step 1005 is the same as the processing of step 909 and step 910 of FIG.
  • the information processing apparatus 301 performs the person identification process (step 1003).
  • the person identification process is the same as the process of step 902, step 903, step 907, and step 908 of FIG. Then, the information processing apparatus 301 performs the processes of steps 1004 and 1005.
  • the information processing device 301 of FIG. 3 it is also possible to detect a plurality of persons including the authentication target person by using another sensor instead of the image pickup device 303.
  • another sensor for example, a motion sensor using infrared rays, ultrasonic waves, or visible light, a distance sensor, or the like can be used.
  • the person detection unit 314 detects a person from the detection result of another sensor and generates position information 335 indicating the position of each person.
  • image processing for detecting a person becomes unnecessary, so that the processing load can be reduced.
  • the configuration of the information processing device 101 of FIG. 1 and the information processing device 301 of FIG. 3 is only an example, and some components may be omitted or changed depending on the use or conditions of the information processing device.
  • the registered biometric information 331 and the registered face image information 332 may be stored in a database outside the information processing device 301.
  • the information processing apparatus 301 acquires the registered biometric information 331 and the registered facial image information 332 from an external database and stores them in the storage unit 311.
  • FIGS. 2, 9 and 10 are merely examples, and some processes may be omitted or changed depending on the configuration or conditions of the information processing device 101 or the information processing device 301.
  • the captured image shown in FIG. 4 is only an example, and the captured image changes depending on the person existing in the imaging area of the image pickup apparatus 303.
  • the time change of the position coordinate x shown in FIGS. 5 to 8 is only an example, and the position coordinate x changes according to the image 334.
  • the calculation formulas of the formulas (1) to (10) are only examples, and the information processing apparatus 301 may perform the biometric authentication process using another calculation formula.
  • FIG. 11 shows a hardware configuration example of the information processing device 101 of FIG. 1 and the information processing device 301 of FIG.
  • the information processing device of FIG. 11 includes a CPU (Central Processing Unit) 1101, a memory 1102, an input device 1103, an output device 1104, an auxiliary storage device 1105, a medium drive device 1106, and a network connection device 1107. These components are hardware and are connected to each other by bus 1108.
  • the biosensor 302 and the image pickup device 303 of FIG. 3 may be connected to the bus 1108.
  • the memory 1102 is, for example, a semiconductor memory such as a ROM (Read Only Memory), a RAM (Random Access Memory), or a flash memory, and stores a program and data used for processing.
  • the memory 1102 may operate as the storage unit 311 of FIG.
  • the CPU 1101 (processor) operates as a reception unit 111, a specific unit 112, a selection unit 113, and an authentication unit 114 in FIG. 1 by executing a program using, for example, the memory 1102.
  • the CPU 1101 includes a biometric information acquisition unit 312, a video acquisition unit 313, a person detection unit 314, a stillness determination unit 315, a face selection unit 316, a face authentication unit 317, and a biometric information selection unit 318 in FIG. It also operates as a biometric authentication unit 319.
  • the input device 1103 is, for example, a keyboard, a pointing device, or the like, and is used for inputting instructions or information from an operator or a user.
  • the output device 1104 is, for example, a display device, a printer, a speaker, or the like, and is used for making an inquiry to an operator or a user or outputting a processing result.
  • the output device 1104 may operate as the output unit 320 of FIG.
  • the processing result may be the authentication result 338.
  • the auxiliary storage device 1105 is, for example, a magnetic disk device, an optical disk device, a magneto-optical disk device, a tape device, or the like.
  • the auxiliary storage device 1105 may be a flash memory or a hard disk drive.
  • the information processing device can store programs and data in the auxiliary storage device 1105 and load them into the memory 1102 for use.
  • the auxiliary storage device 1105 may operate as the storage unit 311 of FIG.
  • the medium drive device 1106 drives the portable recording medium 1109 and accesses the recorded contents.
  • the portable recording medium 1109 is a memory device, a flexible disk, an optical disk, a magneto-optical disk, or the like.
  • the portable recording medium 1109 may be a CD-ROM (Compact Disk Read Only Memory), a DVD (Digital Versatile Disk), a USB (Universal Serial Bus) memory, or the like.
  • the operator or the user can store programs and data in the portable recording medium 1109 and load them into the memory 1102 for use.
  • the computer-readable recording medium that stores the programs and data used for processing is physical (non-temporary) recording, such as memory 1102, auxiliary storage device 1105, or portable recording medium 1109. It is a medium.
  • the network connection device 1107 is a communication interface circuit that is connected to a communication network such as LAN (Local Area Network) and WAN (Wide Area Network) and performs data conversion associated with communication.
  • the information processing device can receive programs and data from an external device via the network connection device 1107, load them into the memory 1102, and use them.
  • the network connection device 1107 may operate as the output unit 320 of FIG.
  • the network connection device 1107 may receive the authentication target biometric information 333 and the video 334 from the biosensor 302 and the image pickup device 303 of FIG. 3 via the communication network, respectively.
  • the information processing apparatus does not have to include all the components shown in FIG. 11, and some components may be omitted depending on the intended use or conditions.
  • the information processing device does not use the portable recording medium 1109 or the communication network
  • the medium driving device 1106 or the network connection device 1107 may be omitted.

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PCT/JP2020/023005 WO2021250845A1 (ja) 2020-06-11 2020-06-11 認証方法、情報処理装置、及び認証プログラム
JP2022530453A JPWO2021250845A1 (https=) 2020-06-11 2020-06-11
CN202080101104.5A CN115668186A (zh) 2020-06-11 2020-06-11 认证方法、信息处理装置、以及认证程序
US17/981,518 US20230059121A1 (en) 2020-06-11 2022-11-07 Authentication method, information processing device, and non-transitory computer-readable storage medium for storing authentication program

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023181267A1 (ja) * 2022-03-24 2023-09-28 富士通株式会社 認証システム、認証クライアント装置、認証サーバ装置および情報処理プログラム
WO2025181903A1 (ja) * 2024-02-27 2025-09-04 日本電気株式会社 認証装置、車両、認証方法及び記録媒体
JP2025529840A (ja) * 2022-11-02 2025-09-09 ▲騰▼▲訊▼科技（深▲セン▼）有限公司 生体決済処理方法、装置、電子機器、及びコンピュータプログラム

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005242677A (ja) * 2004-02-26 2005-09-08 Ntt Comware Corp 複合認証システムおよびその方法ならびにプログラム
JP2007156790A (ja) * 2005-12-05 2007-06-21 Hitachi Omron Terminal Solutions Corp 複数種の生体情報による認証をおこなう認証技術
JP2007154472A (ja) * 2005-12-02 2007-06-21 Hitachi Information & Control Solutions Ltd 指静脈認証装置および指静脈認証方法
JP2013250856A (ja) * 2012-06-01 2013-12-12 Mitsubishi Electric Corp 監視システム
JP2017049867A (ja) * 2015-09-03 2017-03-09 日本電気株式会社 認証装置、防犯システム、認証方法およびプログラム
WO2020070821A1 (ja) 2018-10-03 2020-04-09 富士通株式会社 生体認証装置、生体認証方法、及び生体認証プログラム

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9020207B2 (en) * 2011-06-07 2015-04-28 Accenture Global Services Limited Biometric authentication technology
CA2902093C (en) * 2014-08-28 2023-03-07 Kevin Alan Tussy Facial recognition authentication system including path parameters
CN108701225B (zh) * 2016-02-26 2023-04-04 日本电气株式会社 面部辨识系统、面部辨识方法和存储介质
JP7090008B2 (ja) * 2018-10-18 2022-06-23 株式会社日立製作所 本人確認支援装置および本人確認支援方法
JP6769475B2 (ja) * 2018-12-04 2020-10-14 日本電気株式会社 情報処理システム、認証対象の管理方法、及びプログラム

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005242677A (ja) * 2004-02-26 2005-09-08 Ntt Comware Corp 複合認証システムおよびその方法ならびにプログラム
JP2007154472A (ja) * 2005-12-02 2007-06-21 Hitachi Information & Control Solutions Ltd 指静脈認証装置および指静脈認証方法
JP2007156790A (ja) * 2005-12-05 2007-06-21 Hitachi Omron Terminal Solutions Corp 複数種の生体情報による認証をおこなう認証技術
JP2013250856A (ja) * 2012-06-01 2013-12-12 Mitsubishi Electric Corp 監視システム
JP2017049867A (ja) * 2015-09-03 2017-03-09 日本電気株式会社 認証装置、防犯システム、認証方法およびプログラム
WO2020070821A1 (ja) 2018-10-03 2020-04-09 富士通株式会社 生体認証装置、生体認証方法、及び生体認証プログラム

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"Fujitsu Develops Non-Contact Biometric Integration Technology as an Optimal Solution for a Cashless Society", 4 October 2018, FUJITSU LABORATORIES LTD.
CAO ET AL.: "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields", 2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR, 2017, pages 7291 - 7299, XP055712609, DOI: 10.1109/CVPR.2017.143
See also references of EP4167112A4

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023181267A1 (ja) * 2022-03-24 2023-09-28 富士通株式会社 認証システム、認証クライアント装置、認証サーバ装置および情報処理プログラム
JP2025529840A (ja) * 2022-11-02 2025-09-09 ▲騰▼▲訊▼科技（深▲セン▼）有限公司 生体決済処理方法、装置、電子機器、及びコンピュータプログラム
WO2025181903A1 (ja) * 2024-02-27 2025-09-04 日本電気株式会社 認証装置、車両、認証方法及び記録媒体

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