WO2022148392A1 - 刷脸支付的方法和装置 - Google Patents

刷脸支付的方法和装置 Download PDF

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Publication number
WO2022148392A1
WO2022148392A1 PCT/CN2022/070469 CN2022070469W WO2022148392A1 WO 2022148392 A1 WO2022148392 A1 WO 2022148392A1 CN 2022070469 W CN2022070469 W CN 2022070469W WO 2022148392 A1 WO2022148392 A1 WO 2022148392A1
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Prior art keywords
risk
user
face
payment
data
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English (en)
French (fr)
Inventor
何晓光
李旭
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Alipay Hangzhou Information Technology Co Ltd
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Alipay Hangzhou Information Technology Co Ltd
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Priority to US18/270,459 priority Critical patent/US20240062208A1/en
Publication of WO2022148392A1 publication Critical patent/WO2022148392A1/zh
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/10—Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
    • G06Q20/102—Bill distribution or payments
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/18—Payment architectures involving self-service terminals [SST], vending machines, kiosks or multimedia terminals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/22—Payment schemes or models
    • G06Q20/227—Payment schemes or models characterised in that multiple accounts are available, e.g. to the payer
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • 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

Definitions

  • One or more embodiments of this specification relate to electronic information technology, and in particular, to a method and device for payment by face recognition.
  • Face-scanning payment is a new payment method centered on face recognition.
  • the process of face-swiping payment is very simple. The user does not need to bring a wallet, bank card or mobile phone. When making payment, the user only needs to face the screen of the POS (point-of-sale) machine, and the face-swiping payment system will automatically match the user's face information with the user's identity.
  • the personal account is linked, and the transaction deduction for the user is completed, and the entire transaction process is very convenient.
  • One or more embodiments of this specification describe a method and apparatus for payment by face recognition, which can reduce the waiting time of the user.
  • a method for payment by face-swiping comprising: detecting a triggering event for payment by face-swiping; acquiring a face image; performing identity verification of a user according to the acquired face image; After the verification is passed, the risk data of the user is obtained; the risk data of the user is used to determine whether the payment risk of the transaction is controllable, and if it is controllable, the user is notified that he or she can leave.
  • the detection of a face-swiping payment trigger event includes any one of the following: detecting that a face appears on the screen of the face-swiping device; detecting a click input on the face-swiping payment button, the The face-swiping payment button is located on the screen of the face-swiping device; the key operation corresponding to the face-swiping payment input through the physical keyboard is detected; the eyes of the human face are detected to look at the screen of the face-swiping device; it is detected on the screen of the face-swiping device A human action corresponding to face-swiping payment appears; a voice password corresponding to face-swiping payment is detected.
  • any one of the following is further performed: according to the acquired face image Image, carry out attention recognition, if it is determined that attention is on the screen of the face brushing machine, then continue to perform the user's identity verification according to the obtained face image; judge whether at least two face images are currently obtained.
  • the face image of the user is determined as the face image of the user, and the identity verification of the user is carried out according to the face image of the user; it is detected whether the human body torso appears on the screen of the face brushing machine, and if so, judge the human torso Whether it belongs to the same user as the acquired face image, if so, continue to perform the user's identity verification according to the acquired face image.
  • the performing the identity verification of the user according to the acquired face image includes: performing living body detection according to the acquired face image; if the living body detection passes, then Perform face recognition according to the acquired face image to determine whether the user identity corresponding to the face image can be recognized, and if so, the user's identity verification is passed.
  • the acquiring the risk data of the user includes: acquiring user risk data of N dimensions; wherein, N is a positive integer; and normalizing the user risk data of each dimension processing, to obtain the user risk vector of the dimension; then, using the risk data to determine whether the payment risk of the transaction is controllable includes: using the following formula to calculate the user risk value:
  • R u (X u ) represents the user risk value
  • the method before the notification that the user can leave, the method further includes: acquiring risk data of the face-scanning device; using the risk data of the face-scanning device to determine whether the payment risk of the transaction is controllable, if If yes, continue to execute the notification that the user can leave.
  • the obtaining the risk data of the face brushing machine includes: obtaining the risk data of the face brushing machine in M dimensions; wherein, M is a positive integer; and for each dimension of the face brushing machine risk data Perform normalization processing to obtain the face-scanning machine risk vector of this dimension; then, using the risk data of the face-scanning machine to determine whether the payment risk of the transaction is controllable includes: using the following formula to calculate the risk value of the machine :
  • R d (X d ) represents the risk value of the implement, represents the implement risk vector in the mth dimension, The value of is 0 or 1, and m is any integer from 1 to M; it is judged whether the risk value of the equipment is 1, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the face-scanning device includes any one of the following: risk data of the software environment of the face-scanning device, risk data of the hardware environment of the face-scanning device, and communication Cyber Risk Data.
  • the method before the notification that the user can leave, the method further includes: acquiring risk data of the merchant; using the risk data of the merchant to determine whether the payment risk of the transaction is controllable, and if so, continuing Performing the notification that the user may leave.
  • the acquiring the risk data of the merchant includes: acquiring the merchant risk data of I dimension; wherein, I is a positive integer; and normalizing the merchant risk data of each dimension , to obtain the merchant risk vector of this dimension; then, using the merchant's risk data to determine whether the payment risk of the transaction is controllable includes: using the following formula to calculate the merchant's risk value:
  • R m (X m ) characterizes the risk value of the merchant
  • the merchant risk vector representing the i-th dimension, i is any integer from 1 to 1; it is judged whether the merchant risk value is greater than the second predetermined value, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the merchant includes any one of the following: historical behavior data of the merchant, credit status data of the user, and service level data of the merchant.
  • the risk data of the user includes any one of the following: historical behavior data of the user, statistical data of the spending power of the user, credit status data of the user, and the The user's Sesame credit score.
  • the method further includes: using the user's account information to perform deduction processing; and/or, after using the risk data to determine that the payment risk of the transaction is uncontrollable, further comprising: using the user's account information to perform deduction processing, if the deduction is made If the payment is unsuccessful, the user will be notified that the deduction failed, and if the deduction is successful, the user will be notified that they can leave.
  • a device for payment by face-swiping including: a face-swiping payment initiation module configured to acquire a face image after detecting a triggering event for face-swiping payment; an identity verification module configured to obtain a face image according to the acquired The face image is used to verify the identity of the user; the risk control module is configured to obtain the user's risk data after the user's identity verification is passed; use the user's risk data to determine whether the payment risk of the transaction is controllable; The notification module is configured to notify the user that the user can leave after the risk control module determines that the payment risk of the transaction is controllable.
  • the face-swiping payment activation module is configured to determine that a face-swiping payment trigger event is detected when any one of the following is detected: it is detected that a face appears on the screen of the face-swiping device ; Detect the click input of the face-swiping payment button, which is located on the screen of the face-swiping machine; Detect the key operation corresponding to the face-swiping payment input through the physical keyboard; Detect the eye gaze of the face The screen of the face-swiping device; the human action corresponding to the face-swiping payment is detected on the screen of the face-swiping device; the voice password corresponding to the face-swiping payment is detected.
  • a payment confirmation module configured to perform any one of the following processing: according to the acquired face image, perform attention recognition, if the attention is determined
  • the identity verification module is configured to perform the user's identity verification according to the acquired face images; determine whether at least two face images are currently acquired, and if so, calculate The spatial position data of the face corresponding to each face image relative to the screen of the face brushing machine is used to calculate the probability corresponding to each face object by using the calculated spatial position data, and the face image with the largest probability value is determined as The face image of the user, and trigger the identity verification module to perform the user's identity verification according to the acquired face image of the user; detect whether the human body torso appears on the screen of the face brushing tool, and if so, determine the Whether the torso of the human body and the acquired face image belong to the same user, if so, trigger the identity verification module to perform the user identity verification according to the acquired face image.
  • the risk control module is configured to perform the following processing: acquiring user risk data of N dimensions; wherein, N is a positive integer; and normalizing the user risk data of each dimension Then, the use of the risk data to determine whether the payment risk of the transaction is controllable includes: using the following formula to calculate the user risk value:
  • R u (X u ) represents the user risk value
  • the risk control module is further configured to perform the following processes: acquiring risk data of the face-scanning device; and judging whether the payment risk of the transaction is controllable by using the risk data of the face-scanning device.
  • the risk control module is configured to perform the following processing: acquiring face-scanning device risk data in M dimensions; wherein M is a positive integer; and for each dimension of face-scanning device risk data Perform normalization processing to obtain the face-scanning machine risk vector of this dimension; then, using the risk data of the face-scanning machine to determine whether the payment risk of the transaction is controllable includes: using the following formula to calculate the risk value of the machine :
  • R d (X d ) represents the risk value of the implement, represents the implement risk vector in the mth dimension, The value of is 0 or 1, and m is any integer from 1 to M; it is judged whether the risk value of the equipment is 1, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the face-scanning device includes any one of the following: risk data of the software environment of the face-scanning device, risk data of the hardware environment of the face-scanning device, and communication Cyber Risk Data.
  • the risk control module is further configured to perform the following processing: obtain risk data of the merchant; use the risk data of the merchant to determine whether the payment risk of the transaction is controllable, and if so, continue to execute The notification that the user may leave.
  • the risk control module is configured to perform the following processing: the acquiring the risk data of the merchant includes: acquiring the merchant risk data of I dimension; wherein, I is a positive integer; Merchant risk data of one dimension is normalized to obtain the merchant risk vector of this dimension; then, using the merchant's risk data to determine whether the payment risk of the transaction is controllable includes: using the following calculation formula to calculate the merchant Value at Risk:
  • R m (X m ) characterizes the risk value of the merchant
  • the merchant risk vector representing the i-th dimension, i is any integer from 1 to 1; it is judged whether the merchant risk value is greater than the second predetermined value, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the merchant includes any one of the following: historical behavior data of the merchant, credit status data of the user, and service level data of the merchant.
  • the risk data of the user includes any one of the following: historical behavior data of the user, statistical data of the spending power of the user, credit status data of the user, and the The user's Sesame credit score.
  • a debit processing module configured to perform at least one of the following processes: after the risk control module determines that the payment risk of the transaction is controllable , using the acquired account information of the user to perform deduction processing, and if the deduction is unsuccessful, debit the account from the pre-established face-scanning payment pool; and/or, determine in the risk control module After the payment risk of the transaction is uncontrollable, the obtained account information of the user is used for deduction processing. If the deduction is unsuccessful, the user is notified that the deduction failed, and if the deduction is successful, the user is notified that he can leave.
  • a computer-readable storage medium on which a computer program is stored, when the computer program is executed in a computer, the computer is made to execute the method described in any embodiment of the present specification.
  • a computing device including a memory and a processor, where executable code is stored in the memory, and when the processor executes the executable code, the processor described in any embodiment of the present specification is implemented. method.
  • the user can leave. In this way, the user does not need to wait on the spot in the subsequent processing of the acquiring and payment stage, thereby reducing the waiting time of the user.
  • FIG. 1 shows a flowchart of a method for payment by face recognition in an embodiment of the present specification.
  • FIG. 2 shows a schematic structural diagram of a face-scanning payment device in an embodiment of the present specification.
  • FIG. 3 shows another schematic structural diagram of a face-scanning payment device in an embodiment of the present specification.
  • FIG. 4 shows another structural schematic diagram of a face-scanning payment device in an embodiment of the present specification.
  • the face image can be obtained, and the user's identity will be verified.
  • the processing of the acquiring and payment stage is required.
  • the processing includes: the user confirms the payment, obtains the user's corresponding payment code, and deducts the user's transaction according to the payment code. After the acquirer-payment phase is completed and the deduction is successful, the user will be notified that they can leave.
  • the acquirer payment stage is related to the user's ability to pay. That is to say, after the user's identity verification is passed and the user's payment risk is controllable, the user does not need to wait on the spot and can leave at any time, thereby reducing the user's waiting time.
  • FIG. 1 shows a flowchart of a face-scanning payment method according to an embodiment. It can be understood that the method can be performed by any apparatus, device, platform, or device cluster with computing and processing capabilities.
  • the method includes: step 101: detecting a face-swiping payment trigger event; step 103: obtaining a face image; step 105: verifying the user's identity according to the obtained face image; step 107: in the After the user's identity verification is passed, obtain the user's risk data; Step 109 : Use the user's risk data to determine whether the payment risk of the transaction is controllable, and if it is controllable, notify the user to leave.
  • the face-swiping payment triggering event detected in step 101 may include any of the following triggering events: Triggering event 1: It is detected that a face appears on the screen of the face-swiping device.
  • this trigger event 1 if a face appears on the screen of the face-scanning device, it means that the user is standing in front of the face-scanning device, indicating that the user is willing to pay by face-scanning. Therefore, this event can be used as a trigger for face-scanning payment. event to start the process of swiping face payment.
  • the trigger event 1 can not only indicate that the current face-swiping payment process needs to be started, but also indicate that the user has a willingness to pay.
  • the embodiment of this specification can advance the confirmation of the user's willingness to pay to the stage of initiating face-swiping payment, instead of requiring the user in the acquiring payment stage in the prior art after the user's identity verification is passed. Confirm willingness to pay.
  • the embodiments of this specification can simplify the process of face-swiping payment.
  • Triggering event 2 A click input on the face-swiping payment button is detected, and the face-swiping payment button is located on the screen of the face-swiping device.
  • a button for enabling face-swiping payment can be displayed on the screen of the face-swiping device. If the user or the merchant clicks the button on the screen, it can indicate that the process of face-swiping payment currently needs to be started.
  • Triggering event 3 A key operation corresponding to face-swiping payment input through the physical keyboard is detected; in an embodiment of this specification, a key operation on the physical keyboard may be preset to correspond to initiating face-swiping payment. Then, if the user or the merchant performs the key operation on the physical keyboard, it means that the process of face-swiping payment needs to be started currently.
  • Trigger event 4 The eye that detects the face looks at the screen of the face brushing device.
  • the trigger event 3 can not only indicate that the current process of face-swiping payment should be started, but also can advance the confirmation of the user's willingness to pay to the stage of starting face-swiping payment, thereby simplifying the process of face-swiping payment.
  • Triggering event 5 It is detected that a human action corresponding to the face-swiping payment appears on the screen of the face-swiping device.
  • a human action corresponding to face-swiping payment may be pre-determined, for example, the user makes a victory gesture, or the user touches his face with his hand.
  • the pre-agreed human action is used to start the process of face-swiping payment.
  • This processing method can increase the user's interesting experience. Moreover, because it is based on dynamic living human movements, it increases the difficulty of imitation and improves face-swiping payment. security.
  • Triggering event 6 A voice password corresponding to face-swiping payment is detected.
  • a voice password corresponding to face-swiping payment may be pre-agreed, for example, the user speaks "face-swiping payment”. This processing method can increase the user's interesting experience.
  • a face image is acquired.
  • a specific method for acquiring a human image can be the same as that in the prior art, for example, starting a camera on a face brushing device to capture a human face image.
  • step 101 the above-mentioned trigger event 1 or trigger event 3 is used to trigger and start the face-swiping payment process, then in this step 103, the face image obtained in step 101 can also be directly used as the acquisition face image.
  • De-interference processing 1 Carry out attention recognition according to the face image obtained in step 103, if it is determined that the attention of the corresponding face is on the screen of the face brushing device, for example, the eyes look directly at the screen and/or the face is For the screen, etc., it can be shown that the currently obtained face image is correct, that is, the user corresponding to the face image is the user who needs to trade at present, then you can continue to perform the user's identity verification according to the obtained face image in step 105. deal with.
  • De-interference processing 2 according to the face images obtained in step 103, determine whether at least two face images are currently obtained, and if so, calculate the space of the face corresponding to each face image relative to the screen of the face brushing device Position data, such as the position or distance of the face relative to the screen of the face brushing device, and the size of the face on the screen, etc.
  • de-interference processing 3 obtain the person in step 103 After the face image, it is detected whether the human body torso appears on the screen of the face brushing device at the same time. If so, it is judged whether the human torso and the acquired face image belong to the same user. If so, it means that the user is indeed standing in front of the face brushing device. , the user currently in need of transaction appears on the screen, rather than a protruding interfering face, the process of performing the user's identity verification according to the acquired face image in step 105 can be continued.
  • the user's identity verification is performed according to the acquired face image, which may specifically include: firstly, performing living body detection according to the obtained face image; secondly, if the living body detection is passed, it indicates the current face
  • the image is not a pre-prepared static image of counterfeiting, but a real face image collected from the scene.
  • face recognition is performed according to the obtained face image to determine whether the user identity corresponding to the face image can be recognized. If yes, it means that the user's identity is determined. For example, it is recognized that the face image corresponds to the user Zhang San whose ID number is A. In this way, the user's identity verification is passed.
  • step 107 and step 109 after the user's identity verification is passed, the user's risk data will be obtained, and according to the risk data, it will be judged whether the payment risk of the transaction is controllable, if it is controllable, it means that the user does not need to be on site After waiting for the successful payment and deduction, you can notify the user to leave.
  • the user's risk data can be obtained from multiple dimensions and judged.
  • user risk data of N dimensions is obtained; wherein, N is a positive integer, preferably, N can be a natural number greater than 1; and the user risk data of each dimension is normalized processing, to obtain the user risk vector of the dimension, and the user risk vector is a value in the range of 0 to 1;
  • the specific implementation process of using the risk data to determine whether the payment risk of the transaction is controllable includes: Use the following formula 1 to calculate the user risk value:
  • R u (X u ) represents the user risk value, represents the user risk vector in the nth dimension, The value of is a value in the range of 0 to 1; n is any integer from 1 to N; it is judged whether the calculated user risk value is greater than the first predetermined value, and if so, it is determined that the payment risk of the transaction is controllable.
  • the first predetermined value may be set to a value greater than or equal to 0 and less than 1 according to service requirements.
  • the user risk vector of multiple dimensions when N is a positive integer greater than 1, the user risk vector of multiple dimensions is used. Therefore, when considering multiple dimensions, it is equivalent that the risk will be allocated to the multiple dimensions, then , the calculated user risk value should be less than the value of the user risk vector corresponding to each dimension, so the user risk vectors of N dimensions are multiplied, which is equivalent to performing risk allocation processing, that is, risk reduction processing. After the multiplication process (that is, the risk reduction process), the obtained value will be relatively small, and the multiplied value can be re-processed. The power calculation is equivalent to enlarging the value of the multiplication process (that is, the risk reduction process), so that the payment risk of the transaction can be more clearly and differentiated through the enlarged value.
  • the constant a>1 can make the final result obtained by calculation formula 1 to be amplified by more multiples, so as to further reflect the payment risk of the transaction.
  • the user's risk data includes any one of the following: the user's historical behavior data, the user's spending power statistics, the user's credit status data, and the user's Sesame Credit score.
  • the transaction risk of face-swiping payment can come from any one of the user, the face-swiping machine, and the merchant.
  • the user For example, the user’s historical payment situation is poor, the face-scanning machine is attacked with viruses, and the merchant has fraudulent transactions, etc., all of which will lead to uncontrollable transaction risks of face-scanning payment. Therefore, it is possible to judge whether the transaction risk is controllable from the perspectives of users, face-scanning machines and merchants.
  • the implementation process of determining whether the payment risk of a transaction is controllable has been described from the perspective of the user.
  • the following describes the implementation process of determining whether the payment risk of a transaction is controllable from the perspective of the face-scanning machine and the perspective of the merchant.
  • the angle of the face brushing device before notifying the user that the user can leave in the above step 109, further perform: step A1, obtaining the risk data of the face brushing device; step B1, using the risk data of the face brushing device to determine whether the payment risk of the transaction is controllable, if yes , then continue to perform the process of notifying the user that the user can leave in step 109 .
  • acquiring the risk data of the face brushing machine in step A1 may include: acquiring the risk data of the face brushing machine in M dimensions; wherein M is a positive integer; and for each dimension of the risk data of the face brushing machine All are normalized to obtain the risk vector of the face brushing machine in this dimension; the value of the face brushing machine risk vector of each dimension is 0 or 1, that is, it is either a value of 0, which means that the risk is uncontrollable, or it means that the risk is controllable.
  • step B1 use the risk data of the face brushing device to determine whether the payment risk of the transaction is controllable, including: using the following calculation formula 2, calculating the risk value of the device:
  • R d (X d ) represents the risk value of the implement, represents the implement risk vector in the mth dimension, The value of is 0 or 1, and m is any integer from 1 to M;
  • Step 109 Determine whether the calculated risk value of the machine is 1. If it is 1, it is determined that the payment risk of the transaction is controllable, and the process of notifying the user to leave in step 109 can be performed; if it is not 1, it is 0. Determine the transaction If the payment risk is uncontrollable, the user will not be notified that they can leave.
  • Equation 2 the implement risk vector There are only two values, 0 or 1, with no intermediate values. This is because, no matter in which dimension the face-scanning device generates risks, the transaction must be impossible. For example, the value of the risk vector of the device in one dimension is 0, which may indicate that the software environment of the face-scanning device has been attacked by hackers. down, no transaction can be made.
  • the risk data of the face-scanning device may include any one of the following: risk data of the software environment of the face-scanning device, risk data of the hardware environment of the face-scanning device, and communication network risk data.
  • step A2 Before notifying the user that the user can leave in the above step 109, further execute: step A2, obtain the risk data of the merchant; step B2, use the risk data of the merchant to determine whether the payment risk of the transaction is controllable, if so, proceed to step 109 The process of notifying users that they can leave.
  • acquiring the risk data of the merchant in step A2 may include: acquiring the merchant risk data of I dimensions; wherein, I is a positive integer; and normalizing the merchant risk data of each dimension , obtain the merchant risk vector of the dimension, and the merchant risk vector of each dimension is a value in the range of 0 to 1; then in step B2, use the merchant's risk data to determine whether the payment risk of the transaction is controllable, including: using the following calculation Formula 3, calculate the merchant's risk value:
  • R m (X m ) represents the merchant's risk value, represents the merchant risk vector of the i-th dimension, is a value in the range of 0 to 1, and i is any integer in the range of 1 to 1; it is judged whether the merchant risk value is greater than the second predetermined value, and if so, it is determined that the payment risk of the transaction is controllable.
  • the second predetermined value may be set to a value greater than or equal to 0 and less than 1 according to service requirements.
  • the merchant risk vector of multiple dimensions when I is a positive integer greater than 1, the merchant risk vector of multiple dimensions is used. Therefore, when considering multiple dimensions, it is equivalent that the risk will be allocated to the multiple dimensions, then , the calculated merchant risk value should be less than the risk value corresponding to each dimension, so the merchant risk vector of I dimension is multiplied, which is equivalent to carrying out the risk allocation process, that is, the risk reduction process. After the multiplication process (that is, the risk reduction process), the obtained value will be relatively small, and the multiplied value can be re-processed. The power calculation is equivalent to appropriately amplifying the value of the multiplication process (that is, the risk reduction process), so that the payment risk of the transaction can be more clearly and differentiated through the amplified value. .
  • the constant b>1 can make the final result obtained by calculation formula 3 be amplified by more multiples, so as to further reflect the payment risk of the transaction from the perspective of the merchant.
  • the risk data of the merchant includes any one of the following: historical behavior data of the merchant, credit status data of the user, and service level data of the merchant.
  • the The power calculation, in the above calculation formula 3, in order to carry out the enlargement processing, is Quadratic calculation.
  • the enlargement effect in the formula 1 will be much greater than that in the formula 3. This is because, in actual business implementation, the judgment result of user payment risk is generally more important than the judgment result of merchant payment risk. By increasing the magnification, the importance of user payment risk can be more prominent.
  • the user's perspective, the face-swiping device's perspective, and the merchant's perspective can be combined to determine whether the payment risk of the transaction is controllable. Specifically, the calculation results of the above three calculation formulas are multiplied together. , if the obtained value is greater than the third predetermined value (the third predetermined value can be set to a value greater than or equal to 0 and less than 1 according to business needs), it can be considered that the payment risk of the transaction is controllable, and the user can be notified Leave, otherwise, the payment risk of the transaction is considered uncontrollable, and the user will not be notified that they can leave.
  • the third predetermined value can be set to a value greater than or equal to 0 and less than 1 according to business needs
  • the embodiments of this specification implement the process of determining whether the payment risk of the transaction is controllable.
  • the processing of the acquiring payment stage provided by the embodiment of this specification may be further performed, including: using the acquired account information of the user to perform deduction processing ; If the deduction is unsuccessful, the deduction will be made from the account of the pre-established face-scanning payment pool. This kind of processing, because the platform that performs the payment risk judgment has already judged that the payment risk of the transaction is controllable. If the subsequent deduction is unsuccessful, the platform can bear the loss, that is, from the account in the pre-established face-swiping payment pool.
  • Deductions are performed to enable merchants to successfully acquire orders and prevent missed orders, thereby transferring the risk of money loss from unsuccessful deductions from merchants to the platform, and merchants do not need to bear the risk of unsuccessful deductions.
  • merchants can enjoy the benefits of risk transfer of money loss without any contract processing with the platform.
  • the processing in the existing acquiring and payment stage can be further performed, including: using the acquired account information of the user to perform deduction processing. If the payment is unsuccessful, the user will be notified that they cannot leave, and the deduction is unsuccessful. If the deduction is successful, the user will be notified that they can leave.
  • At least the following beneficial effects can be obtained: 1. After the user's identity verification is passed, and it is judged that the payment risk of the transaction is controllable, the When processing in the acquirer-payment phase, the user is notified that they can leave. In this way, the user does not need to wait on the spot in the subsequent processing of the acquiring and payment stage, thereby reducing the waiting time of the user.
  • the confirmation of the user's willingness to pay can be advanced to the stage of starting face-swiping payment.
  • the user only needs to swipe his face once, instead of swiping his face once in the startup stage in the prior art. , swipe your face again to confirm the willingness to pay in the acquiring payment stage, and swipe your face twice in total. This simplifies the face payment process.
  • the risk allocation process can be performed first, that is, the risk value reduction process, and then the risk value amplification process for prompting the risk. In this way, the calculated risk value can be made. More reasonable and easier to characterize the degree of risk.
  • the platform can bear the loss, that is, deduct money from the account of the pre-established face-scanning payment pool, so that the merchant can Acquiring is successful, preventing missed orders, thereby transferring the risk of money loss from unsuccessful deductions from merchants to the platform, and merchants do not need to bear the risk of unsuccessful deductions.
  • merchants can enjoy the benefits of risk transfer of money loss without any contract processing with the platform.
  • a face-swiping payment device including: a face-swiping payment initiation module 201 configured to acquire a face image after detecting a face-swiping payment trigger event;
  • the verification module 202 is configured to verify the user's identity according to the acquired face image;
  • the risk control module 203 is configured to acquire the user's risk data after the user's identity verification is passed;
  • the risk data determines whether the payment risk of the transaction is controllable;
  • the notification module 204 is configured to notify the user that they can leave after the risk control module 203 determines that the payment risk of the transaction is controllable.
  • the face-swiping payment initiation module 201 is configured to determine that a face-swiping payment trigger event is detected when any one of the following is detected: it is detected that a face-swiping device appears on the screen face; detected the click input on the face-swiping payment button, which is located on the screen of the face-swiping machine; detected the key operation corresponding to the face-swiping payment input through the physical keyboard; detected the eyes of the face Look at the screen of the face-swiping device; detect that a human action corresponding to the face-swiping payment appears on the screen of the face-swiping device; and detect a voice password corresponding to the face-swiping payment.
  • a payment confirmation module 301 may be further included; the payment confirmation module 301 is configured to perform any one of the following processes: according to the acquired face image, pay attention to Force recognition, if it is determined that attention is on the screen of the face brushing device, then triggering the identity verification module 202 to perform the verification of the user's identity according to the acquired face image; determine whether at least two people are currently acquired If it is a face image, then calculate the spatial position data of the face corresponding to each face image relative to the screen of the face brushing device, use the calculated spatial position data to calculate the probability corresponding to each face object, and calculate the probability of The face image with the largest value is determined as the face image of the user, and triggers the identity verification module 202 to carry out the user's identity verification according to the acquired face image of the user; it is detected whether there is an appearance on the screen of the face brushing device Human body torso, if yes, then judge whether the human body torso and the acquired face
  • the identity verification module 202 is configured to perform the following processing: perform living body detection according to the acquired face image; if the living body detection passes, then according to the acquired The face image is subjected to face recognition, and it is judged whether the user identity corresponding to the face image can be recognized, and if yes, the identity verification of the user is passed.
  • the risk control module 203 is configured to perform the following processing: obtain user risk data of N dimensions; wherein, N is a positive integer; and perform the following processing on the user risk data of each dimension Normalization processing is performed to obtain the user risk vector of the dimension; the user risk vector of each dimension is any value in the range of 0 to 1; then, the use of the risk data to determine whether the payment risk of the transaction is controllable, including : Calculate the user risk value using the following formula:
  • R u (X u ) represents the user risk value, represents the user risk vector in the nth dimension, is a value in the range of 0 to 1; n is any integer from 1 to N; determine whether the user risk value is greater than a first predetermined value, and if so, determine that the payment risk of the transaction is controllable.
  • the risk control module 203 is further configured to perform the following processing: acquiring risk data of the face brushing device;
  • Whether the payment risk of the transaction is controllable is determined by using the risk data of the face-scanning device.
  • the risk control module 203 is configured to perform the following processing: acquiring risk data of face brushing equipment in M dimensions; wherein, M is a positive integer; and for each dimension of face brushing equipment The risk data are all normalized to obtain the face-scanning machine risk vector of this dimension; the value of the face-scanning machine risk vector of each dimension is 0 or 1; then, the use of the face-scanning machine risk data is used to judge the transaction. Whether the payment risk is controllable, including: using the following formula to calculate the risk value:
  • R d (X d ) represents the risk value of the implement, represents the implement risk vector in the mth dimension, The value of is 0 or 1, and m is any integer from 1 to M; it is judged whether the risk value of the equipment is 1, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the face-scanning device includes any one of the following: risk data of the software environment of the face-scanning device, risk data of the hardware environment of the face-scanning device, and Communication cyber risk data.
  • the risk control module 203 is further configured to perform the following processing: obtain the risk data of the merchant; determine whether the payment risk of the transaction is controllable by using the risk data of the merchant, and if so, then Proceed to the notification that the user can leave.
  • the risk control module 203 is configured to perform the following processing: the acquiring the risk data of the merchant includes: acquiring the merchant risk data of I dimension; wherein, I is a positive integer; and The merchant risk data of each dimension is normalized to obtain the merchant risk vector of this dimension; the value of the merchant risk vector of each dimension is any value from 0 to 1; then, the use of the merchant's risk vector The risk data determines whether the payment risk of the transaction is controllable, including: using the following formula to calculate the merchant's risk value:
  • R m (X m ) characterizes the risk value of the merchant, represents the implement risk vector of the i-th dimension, The value of is any value from 0 to 1; i is any integer from 1 to 1; it is judged whether the merchant risk value is greater than the second predetermined value, and if so, it is determined that the payment risk of the transaction is controllable.
  • the risk data of the merchant includes any one of the following: historical behavior data of the merchant, credit status data of the user, and service level data of the merchant.
  • the risk data of the user includes any one of the following: historical behavior data of the user, statistical data of the consumption ability of the user, credit status data of the user, and data of the user's credit status. the user's Sesame Credit score.
  • the apparatus proposed in this specification further includes: a chargeback processing module 401; the chargeback processing module 401 is configured to perform at least one of the following processes: the risk control module 203 judges After the payment risk of the outgoing transaction is controllable, use the obtained account information of the user to perform deduction processing. If the deduction is unsuccessful, deduct the payment from the account in the pre-established face-scanning payment pool; in the risk control module 203 After judging that the payment risk of the transaction is uncontrollable, use the acquired account information of the user to perform deduction processing, if the deduction is unsuccessful, notify the user that the deduction failed, and if the deduction is successful, notify the user to leave.
  • the risk control module 203 judges After the payment risk of the outgoing transaction is controllable, use the obtained account information of the user to perform deduction processing. If the deduction is unsuccessful, deduct the payment from the account in the pre-established face-scanning payment pool; in the risk control module 203 After judging that the payment risk of the transaction is uncontrollable, use the acquired account information of the
  • the above-mentioned device for face-scanning payment may be integrated into a face-scanning device, or may also be integrated into an independent device connected to the face-scanning device.
  • a computer-readable storage medium on which a computer program is stored, when the computer program is executed in a computer, the computer is made to execute the method described in any embodiment of the present specification .
  • a computing device including a memory and a processor, wherein executable codes are stored in the memory, and when the processor executes the executable codes, any embodiment of the present specification is implemented method described in .
  • the functions described in the present invention may be implemented in hardware, software, firmware, or any combination thereof.
  • the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.

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Abstract

本说明书实施例提供了刷脸支付的方法和装置。该方法首先确定是否检测到刷脸支付触发事件，如果检测到，则会获取人脸图像，并根据获取的所述人脸图像进行用户的身份核验；在所述用户的身份核验通过后，获取所述用户的风险数据；利用所述用户的风险数据判断交易的支付风险是否可控，如果可控，通知用户可离开。

Description

刷脸支付的方法和装置 技术领域
本说明书一个或多个实施例涉及电子信息技术，尤其涉及刷脸支付的方法和装置。
背景技术
刷脸支付是一种以人脸识别为核心的新型支付方式。刷脸支付的过程非常简单，用户不需要带钱包、银行卡或手机，支付时只需要用户面对POS(销售点)机的屏幕，刷脸支付系统就会自动将用户面部信息与该用户的个人账户相关联，并完成针对该用户的交易扣款，整个交易过程十分便捷。
但是，目前的刷脸支付过程中，用户需要等到刷脸支付的交易扣款成功后，才能离开，增加了用户的等待时间。因此，希望能有改进的方案，能够减少用户在刷脸支付过程中的等待时间。
发明内容
本说明书一个或多个实施例描述了刷脸支付的方法和装置，能够减少用户的等待时间。
根据第一方面，提供了一种刷脸支付的方法，包括：检测到刷脸支付触发事件；获取人脸图像；根据获取的所述人脸图像进行用户的身份核验；在所述用户的身份核验通过后，获取所述用户的风险数据；利用所述用户的风险数据判断交易的支付风险是否可控，如果可控，通知用户可离开。
在本说明书的一个实施例中，所述检测到刷脸支付触发事件包括如下中的任一项：检测到刷脸机具的屏幕上出现人脸；检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上；检测到通过物理键盘输入的对应于刷脸支付的按键操作；检测到人脸的眼部注视刷脸机具的屏幕；检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作；检测到对应于刷脸支付的语音口令。
在本说明书的一个实施例中，在所述获取人脸图像之后，并在所述根据获取的人脸图像进行用户的身份核验之前，进一步执行如下中的任一项：根据所获取的人脸图像，进行注意力识别，如果确定注意力在所述刷脸机具的屏幕上，则继续执行所述根据获取 的所述人脸图像进行用户的身份核验；判断当前是否获取了至少两个人脸图像，如果是，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，利用计算出的空间位置数据计算每一个人脸图像对应的概率，将概率值最大的人脸图像确定为所述用户的人脸图像，并根据该用户的人脸图像进行所述用户的身份核验；检测刷脸机具的屏幕上是否出现人体躯干，如果是，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则继续执行所述根据获取的所述人脸图像进行用户的身份核验。
在本说明书的一个实施例中，所述根据获取的所述人脸图像进行所述用户的身份核验，包括：根据所获取的所述人脸图像进行活体检测；如果所述活体检测通过，则根据所获取的所述人脸图像进行人脸识别，判断能否识别出对应于所述人脸图像的用户身份，如果能，则所述用户的身份核验通过。
在本说明书的一个实施例中，所述获取所述用户的风险数据包括：获取N个维度的用户风险数据；其中，N为正整数；以及对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量；则，所述利用所述风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算用户风险值：
Figure PCTCN2022070469-appb-000001
其中
Figure PCTCN2022070469-appb-000002
常量a>1
其中，R u(X u)表征所述用户风险值，
Figure PCTCN2022070469-appb-000003
表征第n个维度的用户风险向量，n为1至N中的任意一个整数；判断所述用户风险值是否大于第一预定值，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，在所述通知所述用户可离开之前，进一步包括：获取刷脸机具的风险数据；利用所述刷脸机具的风险数据判断交易的支付风险是否可控，如果是，则继续执行所述通知所述用户可离开。
在本说明书的一个实施例中，所述获取刷脸机具的风险数据，包括：获取M个维度的刷脸机具风险数据；其中，M为正整数；以及对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；则，所述利用所述刷脸机具的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算机具风险值：
Figure PCTCN2022070469-appb-000004
其中，R d(X d)表征所述机具风险值，
Figure PCTCN2022070469-appb-000005
表征第m个维度的机具风险向量，
Figure PCTCN2022070469-appb-000006
的值 为0或1，m为1至M中的任意一个整数；判断所述机具风险值是否为1，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，所述刷脸机具的风险数据包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以及通信网络风险数据。
在本说明书的一个实施例中，在所述通知所述用户可离开之前，进一步包括：获取商户的风险数据；利用所述商户的风险数据判断交易的支付风险是否可控，如果是，则继续执行所述通知所述用户可离开。
在本说明书的一个实施例中，所述获取商户的风险数据，包括：获取I个维度的商户风险数据；其中，I为正整数；以及对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量；则，所述利用所述商户的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算商户风险值：
Figure PCTCN2022070469-appb-000007
其中
Figure PCTCN2022070469-appb-000008
常量b>1
其中，R m(X m)表征所述商户风险值，
Figure PCTCN2022070469-appb-000009
表征第i个维度的商户风险向量，i为1至I中的任意一个整数；判断所述商户风险值是否大于第二预定值，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，所述商户的风险数据包括如下中的任一项：商户的历史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
在本说明书的一个实施例中，所述用户的风险数据包括如下中的任一项：所述用户的历史行为数据、所述用户的消费能力统计数据、所述用户的信用状态数据以及所述用户的芝麻信用分数。
在本说明书的一个实施例中，在利用所述风险数据判断出交易的支付风险可控之后，进一步包括：利用所述用户的账户信息进行扣款处理；如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款；和/或，在利用所述风险数据判断出交易的支付风险不可控之后，进一步包括：利用所述用户的账户信息进行扣款处理，如果扣款不成功，则通知用户扣款失败，如果扣款成功，则通知用户可离开。
根据第二方面，提供了一种刷脸支付的装置，包括：刷脸支付启动模块，配置为在 检测到刷脸支付触发事件后，获取人脸图像；身份核验模块，配置为根据获取的所述人脸图像进行用户的身份核验；风险控制模块，配置为在所述用户的身份核验通过后，获取所述用户的风险数据；利用所述用户的风险数据判断交易的支付风险是否可控；通知模块，配置为在所述风险控制模块判断出交易的支付风险可控之后，通知所述用户可离开。
在本说明书的一个实施例中，所述刷脸支付启动模块被配置为在检测到如下中的任一项时，确定检测到刷脸支付触发事件：检测到刷脸机具的屏幕上出现人脸；检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上；检测到通过物理键盘输入的对应于刷脸支付的按键操作；检测到人脸的眼部注视刷脸机具的屏幕；检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作；检测到对应于刷脸支付的语音口令。
在本说明书的一个实施例中，进一步包括：支付确认模块；所述支付确认模块被配置为执行如下中的任一项处理：根据所获取的人脸图像，进行注意力识别，如果确定注意力在所述刷脸机具的屏幕上，则触发所述身份核验模块执行所述根据获取的所述人脸图像进行用户的身份核验；判断当前是否获取了至少两个人脸图像，如果是，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，利用计算出的空间位置数据计算每一个人脸对象对应的概率，将概率值最大的人脸图像确定为所述用户的人脸图像，并触发所述身份核验模块执行根据获取的所述用户的人脸图像进行用户的身份核验；检测刷脸机具的屏幕上是否出现人体躯干，如果是，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则触发所述身份核验模块执行所述根据获取的所述人脸图像进行用户的身份核验。
在本说明书的一个实施例中，所述风险控制模块被配置为执行如下处理：获取N个维度的用户风险数据；其中，N为正整数；以及对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量；则，所述利用所述风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算用户风险值：
Figure PCTCN2022070469-appb-000010
其中
Figure PCTCN2022070469-appb-000011
常量a>1
其中，R u(X u)表征所述用户风险值，
Figure PCTCN2022070469-appb-000012
表征第n个维度的用户风险向量，n为1至N中的任意一个整数；判断所述用户风险值是否大于第一预定值，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，所述风险控制模块进一步被配置为执行如下处理：获取刷脸机具的风险数据；利用所述刷脸机具的风险数据判断交易的支付风险是否可控。
在本说明书的一个实施例中，所述风险控制模块被配置为执行如下处理：获取M个维度的刷脸机具风险数据；其中，M为正整数；以及对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；则，所述利用所述刷脸机具的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算机具风险值：
Figure PCTCN2022070469-appb-000013
其中，R d(X d)表征所述机具风险值，
Figure PCTCN2022070469-appb-000014
表征第m个维度的机具风险向量，
Figure PCTCN2022070469-appb-000015
的值为0或1，m为1至M中的任意一个整数；判断所述机具风险值是否为1，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，所述刷脸机具的风险数据包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以及通信网络风险数据。
在本说明书的一个实施例中，所述风险控制模块进一步被配置为执行如下处理：获取商户的风险数据；利用所述商户的风险数据判断交易的支付风险是否可控，如果是，则继续执行所述通知所述用户可离开。
在本说明书的一个实施例中，所述风险控制模块被配置为执行如下处理：所述获取商户的风险数据，包括：获取I个维度的商户风险数据；其中，I为正整数；以及对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量；则，所述利用所述商户的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算商户风险值：
Figure PCTCN2022070469-appb-000016
其中
Figure PCTCN2022070469-appb-000017
常量b>1
其中，R m(X m)表征所述商户风险值，
Figure PCTCN2022070469-appb-000018
表征第i个维度的商户风险向量，i为1至I中的任意一个整数；判断所述商户风险值是否大于第二预定值，如果是，则确定所述交易的支付风险可控。
在本说明书的一个实施例中，所述商户的风险数据包括如下中的任一项：商户的历 史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
在本说明书的一个实施例中，所述用户的风险数据包括如下中的任一项：所述用户的历史行为数据、所述用户的消费能力统计数据、所述用户的信用状态数据以及所述用户的芝麻信用分数。
在本说明书的一个实施例中，进一步包括：扣款处理模块；所述扣款处理模块被配置为执行如下处理中的至少一项：在所述风险控制模块判断出交易的支付风险可控之后，利用获取的所述用户的账户信息进行扣款处理，如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款；和/或，在所述风险控制模块判断出交易的支付风险不可控之后，利用获取的所述用户的账户信息进行扣款处理，如果扣款不成功，则通知用户扣款失败，如果扣款成功，则通知用户可离开。
根据第三方面，提供了一种计算机可读存储介质，其上存储有计算机程序，当所述计算机程序在计算机中执行时，令计算机执行本说明书任一实施例所述的方法。
根据第四方面，提供了一种计算设备，包括存储器和处理器，所述存储器中存储有可执行代码，所述处理器执行所述可执行代码时，实现本说明书任一实施例所述的方法。
根据本说明书实施例提供的刷脸支付的方法和装置，一旦在用户的身份核验通过，并且判断出交易的支付风险可控之后，用户就可以离开。这样，后续的收单支付阶段的处理中，用户就无需在现场等待，从而减少了用户的等待时间。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出了本说明书一个实施例中刷脸支付的方法的流程图。
图2示出了本说明书一个实施例中刷脸支付装置的一种结构示意图。
图3示出了本说明书一个实施例中刷脸支付装置的另一种结构示意图。
图4示出了本说明书一个实施例中刷脸支付装置的又一种结构示意图。
具体实施方式
下面结合附图，对本说明书提供的方案进行描述。
在现有的刷脸支付过程中，用户刷脸后，可以获取到人脸图像，则会对用户的身份进行核验，在核验通过后，需要进行收单支付阶段的处理，该收单支付阶段的处理包括：用户确认支付，获取用户对应的付款码，根据付款码对用户的交易进行扣款处理。在该收单支付阶段完成且扣款成功后，会通知用户可离开。
可见，在现有的刷脸支付过程中，即使用户的身份核验通过，用户仍然不能离开，而是需要等到整个收单支付阶段完成且扣款成功后，用户方可离开。因此，增加了用户的等待时间。用户等待时间过长，会导致用户体验差，从而限制了业务的发展。
对刷脸支付的过程进行分析可知，在对用户的身份进行核验时，因为涉及对用户的处理，因此，用户需要在现场等待，不能离开。但是，一旦在用户的身份核验通过后，由于用户已经完成刷脸，因此后续的收单支付阶段的处理与用户是否在现场没有必然联系。该收单支付阶段是与用户的支付能力相关。也就是说，在用户身份核验通过且用户的支付风险可控后，用户就无需在现场等待，随时可以离开，从而减少用户的等待时间。
下面描述本说明书所提供构思的具体实现方式。
图1示出根据一个实施例的刷脸支付方法的流程图。可以理解，该方法可以通过任何具有计算、处理能力的装置、设备、平台、设备集群来执行。参见图1，该方法包括：步骤101：检测到刷脸支付触发事件；步骤103：获取人脸图像；步骤105：根据获取的所述人脸图像进行用户的身份核验；步骤107：在所述用户的身份核验通过后，获取所述用户的风险数据；步骤109：利用所述用户的风险数据判断交易的支付风险是否可控，如果可控，通知所述用户可离开。
可见，在上述图1所示过程中，一旦在用户的身份核验通过，并且判断出交易的支付风险可控之后，用户就可以离开。这样，后续的收单支付阶段的处理中，用户就无需在现场等待，从而减少了用户的等待时间。
下面对图1所示的各个步骤进行说明。
在本说明书的一个实施例中，步骤101中检测到的刷脸支付触发事件可以包括如下任一项的触发事件：触发事件1：检测到刷脸机具的屏幕上出现人脸。
在本触发事件1中，如果刷脸机具的屏幕上出现人脸，则可以说明用户站在了刷脸 机具前，表示用户有刷脸支付的意愿，因此，可以将此事件作为刷脸支付触发事件来启动刷脸支付的流程。
同时，该触发事件1不仅能说明当前需要启动刷脸支付流程，而且也能说明用户有支付意愿。应用该触发事件1后，本说明书的实施例则可以将用户支付意愿的确认提前到启动刷脸支付的阶段，而不是现有技术中在用户身份核验通过后，在收单支付阶段才需要用户确认支付意愿。应用该触发事件1后，本说明书的实施例可以简化刷脸支付的流程。
触发事件2：检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上。
在本说明书的一个实施例中，可以在刷脸机具的屏幕上显示刷脸支付开启的按钮，如果用户或者商户点击了屏幕上的该按钮，则可以说明当前需要启动刷脸支付的流程。
触发事件3：检测到通过物理键盘输入的对应于刷脸支付的按键操作；在本说明书的一个实施例中，可以预先设置物理键盘上的一个按键操作对应于启动刷脸支付。那么，如果用户或者商户在物理键盘上进行该按键操作，则说明当前需要启动刷脸支付的流程。
触发事件4：检测到人脸的眼部注视刷脸机具的屏幕。
在本说明书的一个实施例中，当检测到人脸的眼部注视刷脸机具的屏幕时，也可以说明用户当前将注意力放在了刷脸机具上，可以证明用户有意愿开启刷脸支付的流程。因此，通过该触发事件3不仅能说明当前应该启动刷脸支付的流程，而且也能将用户支付意愿的确认提前到启动刷脸支付的阶段，从而简化了刷脸支付的流程。
触发事件5：检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作。
在本说明书的一个实施例中，可以预先约定一个对应于刷脸支付的人体动作，比如，用户摆出胜利的手势，或者，用户用手摸脸等。通过该预先约定的人体动作来启动刷脸支付的过程，此种处理方式能够增加用户的趣味体验，并且，由于是基于动态的活体人类动作，因此，增加了仿造的难度，提高了刷脸支付的安全性。
触发事件6：检测到对应于刷脸支付的语音口令。
在本说明书的一个实施例中，可以预先约定一个对应于刷脸支付的语音口令，比如，用户说出“刷脸支付”。此种处理方式能够增加用户的趣味体验。
接下来，在步骤103中，获取人脸图像。具体的获取人类图像的方法可以跟现有技 术中相同，比如，启动刷脸机具上的摄像头，从而拍摄出人脸图像。
需要说明的是，如果在步骤101中，是通过上述触发事件1或者触发事件3来触发启动刷脸支付流程，那么，本步骤103中，也可以直接将步骤101中得到的人脸图像作为获取的人脸图像。
在实际的业务实现中，很可能会存在刷脸机具的屏幕上出现非交易的用户的人脸的干扰情况，比如旁人经过刷脸机具时其人脸无意中出现在该屏幕上；再如，进行交易的用户与他人同行时，他人站在该交易的用户的旁边导致屏幕上出现多个人脸。针对此种情况，在本说明书的一个实施例中，在步骤103获取了人脸图像之后，并执行上述步骤105进行用户的身份核验之前，还可以进一步执行如下的去干扰处理中的任一项：去干扰处理1：根据步骤103所获取的人脸图像，进行注意力识别，如果确定对应的人脸的注意力在刷脸机具的屏幕上，比如，眼睛直视屏幕和/或人脸正对屏幕等，都可以说明当前获取的人脸图像正确，即该人脸图像对应的用户正是当前需要交易的用户，则可以继续执行步骤105中根据获取的人脸图像进行用户的身份核验的处理。
去干扰处理2：根据步骤103所获取的人脸图像判断当前是否获取了至少两个人脸图像，如果是，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，比如该人脸相对于刷脸机具屏幕的位置或距离，再如该人脸在屏幕上的大小等，然后，利用计算出的空间位置数据计算每一个人脸图像对应的概率，将概率值最大的人脸图像确定为当前需要交易的用户的人脸图像，并根据该当前需要交易的用户的人脸图像进行步骤105中用户的身份核验；去干扰处理3：在步骤103获取人脸图像之后，检测刷脸机具的屏幕上是否同时出现人体躯干，如果是，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则说明用户确实站在刷脸机具前，屏幕上出现的是当前需要交易的用户，而不是一个探出的干扰的人脸，则可以继续执行步骤105中根据获取的人脸图像进行用户的身份核验的处理。
接下来，在步骤105中，根据获取的人脸图像进行用户的身份核验，具体可以包括：首先，根据所获取的人脸图像进行活体检测；其次，如果活体检测通过，则说明当前的人脸图像不是仿冒的一个预先准备的静态图像，而是真实从现场采集的人脸图像，之后，根据所获取的人脸图像进行人脸识别，判断能否识别出对应于人脸图像的用户身份，如果能，则说明确定了用户身份，比如识别出人脸图像对应的是身份证号为A的用户张三，这样，则用户的身份核验通过。
接下来，在步骤107和步骤109中，在用户的身份核验通过后，会获取用户的风险 数据，并根据该风险数据判断交易的支付风险是否可控，如果可控，则说明用户无需在现场等待支付扣款成功，则可以通知用户离开。
在本说明书的一个实施例中，为了更加全面地评价用户的支付能力，更为准确地判断出交易的支付风险是否可控，可以从多个维度来获取用户的风险数据，并进行判断。具体地，在步骤107中，获取N个维度的用户风险数据；其中，N为正整数，较佳地，N可以为大于1的自然数；并且，对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量，该用户风险向量为0至1范围内的一个数值；相应地，在步骤109中，利用风险数据判断交易的支付风险是否可控的具体实现过程包括：利用如下计算式1，计算用户风险值：
Figure PCTCN2022070469-appb-000019
其中
Figure PCTCN2022070469-appb-000020
常量a>1
其中，R u(X u)表征所述用户风险值，
Figure PCTCN2022070469-appb-000021
表征第n个维度的用户风险向量，
Figure PCTCN2022070469-appb-000022
的值为0至1范围内的一个数值；n为1至N中的任意一个整数；判断计算出的用户风险值是否大于第一预定值，如果是，则确定交易的支付风险可控。
第一预定值可以根据业务需要，设定为大于等于0且小于1的一个数值。
在上述计算式1中，当N为大于1的正整数时，利用的是多个维度的用户风险向量，因此，基于多个维度考虑时，相当于风险会被分摊到该多个维度，那么，计算出的用户风险值应该小于每一个维度对应的用户风险向量的值，所以将N个维度的用户风险向量进行相乘，这样则相当于进行了风险分摊处理，即风险缩小处理。在进行相乘处理(即风险缩小处理)之后，得到的数值会比较小，可以对该相乘后的数值再进行
Figure PCTCN2022070469-appb-000023
次方计算，则相当于对相乘处理(即风险缩小处理)的数值进行放大处理，从而能够通过该放大处理后的数值来更为明显、更为差异化地体现出交易的支付风险。
在上述计算式1中，常量a＞1，可以使得计算式1最终得到的结果被放大的倍数更多，从而更进一步地体现交易的支付风险。
在本说明书的一个实施例中，用户的风险数据包括如下中的任一项：用户的历史行为数据、用户的消费能力统计数据、用户的信用状态数据以及用户的芝麻信用分数。
在实际的业务实现中，刷脸支付的交易风险可以来源于用户、刷脸机具以及商户中的任意一方。比如，用户的历史支付情况较差、刷脸机具被攻击带病毒、商户存在欺诈 交易行为等，都会导致刷脸支付的交易风险不可控。因此，可以从用户、刷脸机具以及商户的角度，分别来判断交易风险是否可控。
在上述本说明书的实施例中，已经从用户的角度描述了确定交易的支付风险是否可控的实现过程。下面分别从刷脸机具的角度以及商户的角度，来分别说明确定交易的支付风险是否可控的实现过程。
刷脸机具角度：在上述步骤109通知用户可离开之前，进一步执行：步骤A1、获取刷脸机具的风险数据；步骤B1、利用刷脸机具的风险数据判断交易的支付风险是否可控，如果是，则继续执行步骤109中通知用户可离开的处理。
在本说明书一个实施例中，步骤A1中获取刷脸机具的风险数据可以包括：获取M个维度的刷脸机具风险数据；其中，M为正整数；以及对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；每一个维度的刷脸机具风险向量的值为0或1，即要么是表示风险不可控的数值0，要么是表示风险可控的数值1，而没有介于0与1之间的中间值；则步骤B1中利用刷脸机具的风险数据判断交易的支付风险是否可控，包括：利用如下计算式2，计算机具风险值：
Figure PCTCN2022070469-appb-000024
其中，R d(X d)表征机具风险值，
Figure PCTCN2022070469-appb-000025
表征第m个维度的机具风险向量，
Figure PCTCN2022070469-appb-000026
的值为0或1，m为1至M中的任意一个整数；
判断计算出的机具风险值是否为1，如果是1，则确定所述交易的支付风险可控，则可以执行步骤109中通知用户可离开的处理；如果不为1，为0，则确定交易的支付风险不可控，则不会通知用户可离开。
在上述计算式2中，机具风险向量
Figure PCTCN2022070469-appb-000027
的值只有两个，0或1，而没有中间值。这是因为，无论刷脸机具在哪个维度上产生风险，交易都必然是不可进行的，比如，一个维度的机具风险向量的值为0，可能表示刷脸机具软件环境被黑客攻击，此种情况下，不能进行交易。
刷脸机具的风险数据可以包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以及通信网络风险数据。
至此，则完成了从刷脸机具角度来判断交易的支付风险是否可控。
商户角度：在上述步骤109通知用户可离开之前，进一步执行：步骤A2、获取商户的风险数据；步骤B2、利用商户的风险数据判断交易的支付风险是否可控，如果是，则继续执行步骤109中通知用户可离开的处理。
在本说明书一个实施例中，步骤A2中获取商户的风险数据可以包括：获取I个维度的商户风险数据；其中，I为正整数；以及对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量，每一个维度的商户风险向量为0至1范围内的一个数值；则步骤B2中，利用商户的风险数据判断交易的支付风险是否可控，包括：利用如下计算式3，计算商户风险值：
Figure PCTCN2022070469-appb-000028
其中
Figure PCTCN2022070469-appb-000029
常量b>1
其中，R m(X m)表征商户风险值，
Figure PCTCN2022070469-appb-000030
表征第i个维度的商户风险向量，
Figure PCTCN2022070469-appb-000031
为0至1范围内的一个数值，i为1至I范围内的任意一个整数；判断商户风险值是否大于第二预定值，如果是，则确定交易的支付风险可控。
第二预定值可以根据业务需要，设定为大于等于0且小于1的一个数值。
在上述计算式3中，当I为大于1的正整数时，利用的是多个维度的商户风险向量，因此，基于多个维度考虑时，相当于风险会被分摊到该多个维度，那么，计算出的商户风险值应该小于每一个维度对应的风险值，所以将I个维度的商户风险向量进行相乘，这样则相当于进行了风险分摊处理，即风险缩小处理。在进行相乘处理(即风险缩小处理)之后，得到的数值会比较小，可以对该相乘后的数值再进行
Figure PCTCN2022070469-appb-000032
次方计算，则相当于对相乘处理(即风险缩小处理)的数值进行适当的放大处理，从而能够通过该放大处理后的数值来更为明显、更为差异化地体现出交易的支付风险。
在上述计算式3中，常量b＞1，可以使得计算式3最终得到的结果被放大的倍数更多，从而更进一步地从商户的角度来体现交易的支付风险。
在本说明书的一个实施例中，商户的风险数据包括如下中的任一项：商户的历史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
在上述计算式1中，为了进行放大处理，进行的是
Figure PCTCN2022070469-appb-000033
次方计算，在上述计算式3中，为了进行放大处理，进行的是
Figure PCTCN2022070469-appb-000034
次方计算。在维度数相等即N＝I的情况下，计算式1中的放大效果将远大于计算式3中的放大效果。这是因为，在实际的业务实现中，用户支 付风险的判断结果一般比商户支付风险的判断结果更为重要，通过增大放大倍数，能够更为突出用户支付风险的重要性。
当然，上述3个计算式中的维度数的值可以不相等，具体看实际业务需求。
另外，在本说明书的一个实施例中，可以综合用户角度、刷脸机具角度以及商户角度来一起判断交易的支付风险是否可控，具体的，则是将上述3个计算式的计算结果相乘，如果得到的数值大于第三预定值(该第三预定值可以根据业务需要，设定为大于等于0且小于1的一个数值)，则可以认为交易的支付风险可控，则可以通知用户可离开，否则，认为交易的支付风险不可控，则不会通知用户可离开。
至此，本说明书实施例则实现了确定交易的支付风险是否可控的处理。
在上述步骤109之后，即在利用风险数据判断出交易的支付风险可控之后，可以进一步进行本说明书实施例提供的收单支付阶段的处理，包括：利用获取的用户的账户信息进行扣款处理；如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款。此种处理，因为执行支付风险判断的平台已经判断出交易的支付风险可控，如果后续扣款不成功，则可以由平台来承担该损失，即从预先设立的刷脸付资金池的账户中进行扣款，使得商户收单成功，防止漏单，从而将扣款不成功的钱款损失风险从商家转移到平台，商家无需承担扣款不成功的风险。并且，商家无需与平台进行任何签约处理，即可以享受钱款损失风险转移的好处。
在上述步骤109之后，即在利用风险数据判断出交易的支付风险不可控之后，可以进一步进行现有的收单支付阶段的处理，包括：利用获取的用户的账户信息进行扣款处理，如果扣款不成功，则通知用户不可离开，扣款失败，如果扣款成功，则通知用户可离开。
综上，根据本说明书提供的一个或多个刷脸支付的方法实施例，可以至少得到如下的有益效果：1、在用户的身份核验通过，并且判断出交易的支付风险可控之后，在未进行收单支付阶段的处理时，就通知用户可离开。这样，后续的收单支付阶段的处理中，用户就无需在现场等待，从而减少了用户的等待时间。
2、本说明书的实施例，可以将用户支付意愿的确认提前到启动刷脸支付的阶段，整个刷脸支付过程中，用户只需要刷脸一次，而不是现有技术中在启动阶段刷脸一次，在收单支付阶段再刷脸一次确认支付意愿，共刷脸两次。从而简化了刷脸支付的流程。
3、能够对获取的人脸图像进行去干扰处理，从而大大降低了刷脸机具的屏幕上出 现非交易用户的人脸时对交易所造成的干扰。
4、能够从用户、刷脸机具、商户三个角度来判断交易的支付风险是否可控，从而使得判断结果更为准确、全面。
5、在从用户或商户角度计算风险值时，可以先进行风险的分摊处理，即风险值缩小处理，然后再进行为了提示风险而进行的风险值放大处理，这样，则可以使得计算的风险值更为合理并且更易于表征风险程度。
6、在平台已经判断出交易的支付风险可控之后，如果后续扣款不成功，则可以由平台来承担该损失，即从预先设立的刷脸付资金池的账户中进行扣款，使得商户收单成功，防止漏单，从而将扣款不成功的钱款损失风险从商家转移到平台，商家无需承担扣款不成功的风险。并且，商家无需与平台进行任何签约处理，即可以享受钱款损失风险转移的好处。
在本说明书的一个实施例中，提出了一种刷脸支付的装置，参见图2，包括：刷脸支付启动模块201，配置为在检测到刷脸支付触发事件后，获取人脸图像；身份核验模块202，配置为根据获取的所述人脸图像进行用户的身份核验；风险控制模块203，配置为在所述用户的身份核验通过后，获取所述用户的风险数据；利用所述用户的风险数据判断交易的支付风险是否可控；通知模块204，配置为在所述风险控制模块203判断出交易的支付风险可控之后，通知用户可离开。
在本说明书提出的装置的一个实施例中，刷脸支付启动模块201被配置为在检测到如下中的任一项时，确定检测到刷脸支付触发事件：检测到刷脸机具的屏幕上出现人脸；检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上；检测到通过物理键盘输入的对应于刷脸支付的按键操作；检测到人脸的眼部注视刷脸机具的屏幕；检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作；检测到对应于刷脸支付的语音口令。
在本说明书提出的装置的一个实施例中，参见图3，可以进一步包括支付确认模块301；支付确认模块301被配置为执行如下中的任一项处理：根据所获取的人脸图像，进行注意力识别，如果确定注意力在所述刷脸机具的屏幕上，则触发所述身份核验模块202执行所述根据获取的所述人脸图像进行用户的身份核验；判断当前是否获取了至少两个人脸图像，如果是，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，利用计算出的空间位置数据计算每一个人脸对象对应的概率，将概率 值最大的人脸图像确定为所述用户的人脸图像，并触发所述身份核验模块202执行根据获取的所述用户的人脸图像进行用户的身份核验；检测刷脸机具的屏幕上是否出现人体躯干，如果是，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则触发所述身份核验模块202执行所述根据获取的所述人脸图像进行用户的身份核验。
在本说明书提出的装置的一个实施例中，身份核验模块202被配置为执行如下处理：根据所获取的所述人脸图像进行活体检测；如果所述活体检测通过，则根据所获取的所述人脸图像进行人脸识别，判断能否识别出对应于所述人脸图像的用户身份，如果能，则所述用户的身份核验通过。
在本说明书提出的装置的一个实施例中，风险控制模块203被配置为执行如下处理：获取N个维度的用户风险数据；其中，N为正整数；以及对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量；每一个维度的用户风险向量为0至1范围内的任意一个数值；则，所述利用所述风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算用户风险值：
Figure PCTCN2022070469-appb-000035
其中
Figure PCTCN2022070469-appb-000036
常量a>1
其中，R u(X u)表征所述用户风险值，
Figure PCTCN2022070469-appb-000037
表征第n个维度的用户风险向量，
Figure PCTCN2022070469-appb-000038
为0至1范围内的一个数值；n为1至N中的任意一个整数；判断所述用户风险值是否大于第一预定值，如果是，则确定所述交易的支付风险可控。
在本说明书提出的装置的一个实施例中，风险控制模块203进一步被配置为执行如下处理：获取刷脸机具的风险数据；
利用所述刷脸机具的风险数据判断交易的支付风险是否可控。
在本说明书提出的装置的一个实施例中，风险控制模块203被配置为执行如下处理：获取M个维度的刷脸机具风险数据；其中，M为正整数；以及对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；每一个维度的刷脸机具风险向量的值为0或者1；则，所述利用所述刷脸机具的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算机具风险值：
Figure PCTCN2022070469-appb-000039
其中，R d(X d)表征所述机具风险值，
Figure PCTCN2022070469-appb-000040
表征第m个维度的机具风险向量，
Figure PCTCN2022070469-appb-000041
的值为0或1，m为1至M中的任意一个整数；判断所述机具风险值是否为1，如果是，则确定所述交易的支付风险可控。
在本说明书提出的装置的一个实施例中，刷脸机具的风险数据包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以及通信网络风险数据。
在本说明书提出的装置的一个实施例中，风险控制模块203进一步被配置为执行如下处理：获取商户的风险数据；利用所述商户的风险数据判断交易的支付风险是否可控，如果是，则继续执行所述通知所述用户可离开。
在本说明书提出的装置的一个实施例中，风险控制模块203被配置为执行如下处理：所述获取商户的风险数据，包括：获取I个维度的商户风险数据；其中，I为正整数；以及对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量；每一个维度的商户风险向量的值为0至1中任意一个数值；则，所述利用所述商户的风险数据判断交易的支付风险是否可控，包括：利用如下计算式，计算商户风险值：
Figure PCTCN2022070469-appb-000042
其中
Figure PCTCN2022070469-appb-000043
常量b>1
其中，R m(X m)表征所述商户风险值，
Figure PCTCN2022070469-appb-000044
表征第i个维度的机具风险向量，
Figure PCTCN2022070469-appb-000045
的值为0至1中任意一个数值；i为1至I中的任意一个整数；判断所述商户风险值是否大于第二预定值，如果是，则确定所述交易的支付风险可控。
在本说明书提出的装置的一个实施例中，商户的风险数据包括如下中的任一项：商户的历史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
在本说明书提出的装置的一个实施例中，用户的风险数据包括如下中的任一项：所述用户的历史行为数据、所述用户的消费能力统计数据、所述用户的信用状态数据以及所述用户的芝麻信用分数。
在本说明书提出的装置的一个实施例中，参见图4，进一步包括：扣款处理模块401；扣款处理模块401被配置为执行如下处理中的至少一项：在所述风险控制模块203判断出交易的支付风险可控之后，利用获取的所述用户的账户信息进行扣款处理，如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款；在风险控制模块203判断出交易的支付风险不可控之后，利用获取的所述用户的账户信息进行扣款处理，如果 扣款不成功，则通知用户扣款失败，如果扣款成功，则通知用户可离开。
在本说明书的一个实施例中，上述刷脸支付的装置可以被集成在刷脸机具中，或者也可以被集成在一个与刷脸机具相连的独立的设备中。
根据另一方面的实施例，还提供一种计算机可读存储介质，其上存储有计算机程序，当所述计算机程序在计算机中执行时，令计算机执行本说明书任一实施例中所描述的方法。
根据再一方面的实施例，还提供一种计算设备，包括存储器和处理器，所述存储器中存储有可执行代码，所述处理器执行所述可执行代码时，实现本说明书任一实施例中所描述的方法。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于装置实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
本领域技术人员应该可以意识到，在上述一个或多个示例中，本发明所描述的功能可以用硬件、软件、固件或它们的任意组合来实现。当使用软件实现时，可以将这些功能存储在计算机可读介质中或者作为计算机可读介质上的一个或多个指令或代码进行传输。
以上所述的具体实施方式，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施方式而已，并不用于限定本发明的保护范围，凡在本发明的技术方案的基础之上，所做的任何修改、等同替换、改进等，均应包括在本发明的保护范围之内。

Claims (27)

  1. 一种刷脸支付的方法，包括：
    检测到刷脸支付触发事件；
    获取人脸图像；
    根据获取的所述人脸图像进行用户的身份核验；
    在所述用户的身份核验通过后，获取所述用户的风险数据；
    利用所述用户的风险数据判断交易的支付风险是否可控；
    如果可控，通知用户可离开。
  2. 根据权利要求1所述的方法，其中，所述检测到刷脸支付触发事件包括如下中的任一项：
    检测到刷脸机具的屏幕上出现人脸；
    检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上；
    检测到通过物理键盘输入的对应于刷脸支付的按键操作；
    检测到人脸的眼部注视刷脸机具的屏幕；
    检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作；
    检测到对应于刷脸支付的语音口令。
  3. 根据权利要求1所述的方法，其中，在所述获取人脸图像之后，并在所述根据获取的人脸图像进行用户的身份核验之前，进一步执行如下中的任一项：
    根据所获取的人脸图像，进行注意力识别，如果确定注意力在所述刷脸机具的屏幕上，则继续执行所述根据获取的所述人脸图像进行用户的身份核验；
    如果当前获取了至少两个人脸图像，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，利用计算出的空间位置数据计算每一个人脸图像对应的概率，将概率值最大的人脸图像确定为所述用户的人脸图像，并根据该用户的人脸图像进行所述用户的身份核验；
    如果检测到刷脸机具的屏幕上出现人体躯干，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则继续执行所述根据获取的所述人脸图像进行用户的身份核验。
  4. 根据权利要求1所述的方法，其中，所述根据获取的所述人脸图像进行所述用户的身份核验，包括：
    根据所获取的所述人脸图像进行活体检测；
    如果所述活体检测通过，则根据所获取的所述人脸图像进行人脸识别，判断能否识别出对应于所述人脸图像的用户身份，如果能，则所述用户的身份核验通过。
  5. 根据权利要求1所述的方法，其中，
    所述获取所述用户的风险数据包括：
    获取N个维度的用户风险数据；其中，N为正整数；以及
    对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量；
    所述利用所述风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算用户风险值：
    Figure PCTCN2022070469-appb-100001
    其中
    Figure PCTCN2022070469-appb-100002
    常量a>1
    其中，R u(X u)表征所述用户风险值，
    Figure PCTCN2022070469-appb-100003
    表征第n个维度的用户风险向量，n为1至N中的任意一个整数；
    如果所述用户风险值大于第一预定值，则确定所述交易的支付风险可控。
  6. 根据权利要求1所述的方法，其中，在所述通知所述用户可离开之前，进一步包括：
    获取刷脸机具的风险数据；
    利用所述刷脸机具的风险数据判断交易的支付风险是否可控。
  7. 根据权利要求6所述的方法，其中，
    所述获取刷脸机具的风险数据，包括：
    获取M个维度的刷脸机具风险数据；其中，M为正整数；以及
    对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；
    所述利用所述刷脸机具的风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算机具风险值：
    Figure PCTCN2022070469-appb-100004
    其中，R d(X d)表征所述机具风险值，
    Figure PCTCN2022070469-appb-100005
    表征第m个维度的机具风险向量，
    Figure PCTCN2022070469-appb-100006
    的值为0或1，m为1至M中的任意一个整数；
    如果所述机具风险值为1，则确定所述交易的支付风险可控。
  8. 根据权利要求6所述的方法，其中，所述刷脸机具的风险数据包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以及通信网络风险数据。
  9. 根据权利要求1所述的方法，其中，在所述通知所述用户可离开之前，进一步包括：
    获取商户的风险数据；
    利用所述商户的风险数据判断交易的支付风险是否可控。
  10. 根据权利要求9所述的方法，其中，
    所述获取商户的风险数据，包括：
    获取I个维度的商户风险数据；其中，I为正整数；以及
    对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量；
    所述利用所述商户的风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算商户风险值：
    Figure PCTCN2022070469-appb-100007
    其中
    Figure PCTCN2022070469-appb-100008
    常量b>1
    其中，R m(X m)表征所述商户风险值，
    Figure PCTCN2022070469-appb-100009
    表征第i个维度的商户风险向量，i为1至I中的任意一个整数；
    如果所述商户风险值大于第二预定值，则确定所述交易的支付风险可控。
  11. 根据权利要求9所述的方法，其中，所述商户的风险数据包括如下中的任一项：商户的历史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
  12. 根据权利要求1至11中任一所述的方法，其中，所述用户的风险数据包括如下中的任一项：所述用户的历史行为数据、所述用户的消费能力统计数据、所述用户的信用状态数据以及所述用户的芝麻信用分数。
  13. 根据权利要求1至11中任一所述的方法，
    在利用所述风险数据判断出交易的支付风险可控之后，进一步包括：利用所述用户的账户信息进行扣款处理；如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款；和/或，
    在利用所述风险数据判断出交易的支付风险不可控之后，进一步包括：利用所述用户的账户信息进行扣款处理，如果扣款不成功，则通知用户扣款失败，如果扣款成功，则通知用户可离开。
  14. 一种刷脸支付的装置，包括：
    刷脸支付启动模块，配置为在检测到刷脸支付触发事件后，获取人脸图像；
    身份核验模块，配置为根据获取的所述人脸图像进行用户的身份核验；
    风险控制模块，配置为在所述用户的身份核验通过后，获取所述用户的风险数据；利用所述用户的风险数据判断交易的支付风险是否可控；
    通知模块，配置为在所述风险控制模块判断出交易的支付风险可控之后，通知所述用户可离开。
  15. 根据权利要求14所述的装置，其中，所述刷脸支付启动模块被配置为在检测到如下中的任一项时，确定检测到刷脸支付触发事件：
    检测到刷脸机具的屏幕上出现人脸；
    检测到对刷脸支付按钮的点击输入，该刷脸支付按钮位于刷脸机具的屏幕上；
    检测到通过物理键盘输入的对应于刷脸支付的按键操作；
    检测到人脸的眼部注视刷脸机具的屏幕；
    检测到刷脸机具的屏幕上出现对应于刷脸支付的人体动作；
    检测到对应于刷脸支付的语音口令。
  16. 根据权利要求14所述的装置，进一步包括：支付确认模块，被配置为执行如下中的任一项处理：
    根据所获取的人脸图像，进行注意力识别，如果确定注意力在所述刷脸机具的屏幕上，则触发所述身份核验模块执行所述根据获取的所述人脸图像进行用户的身份核验；
    如果当前获取了至少两个人脸图像，则计算每一个人脸图像对应的人脸相对于所述刷脸机具的屏幕的空间位置数据，利用计算出的空间位置数据计算每一个人脸对象对应的概率，将概率值最大的人脸图像确定为所述用户的人脸图像，并触发所述身份核验模块执行根据获取的所述用户的人脸图像进行用户的身份核验；
    如果检测到刷脸机具的屏幕上出现人体躯干，则判断该人体躯干与获取的人脸图像是否属于同一个用户，如果属于，则触发所述身份核验模块执行所述根据获取的所述人脸图像进行用户的身份核验。
  17. 根据权利要求14所述的装置，其中，
    所述风险控制模块被配置为执行如下处理：
    获取N个维度的用户风险数据；其中，N为正整数；以及
    对每一个维度的用户风险数据均进行归一化处理，得到该维度的用户风险向量；
    则，所述利用所述风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算用户风险值：
    Figure PCTCN2022070469-appb-100010
    其中
    Figure PCTCN2022070469-appb-100011
    常量a>1
    其中，R u(X u)表征所述用户风险值，
    Figure PCTCN2022070469-appb-100012
    表征第n个维度的用户风险向量，n为1至N中的任意一个整数；
    如果所述用户风险值大于第一预定值，则确定所述交易的支付风险可控。
  18. 根据权利要求14所述的装置，其中，所述风险控制模块进一步被配置为执行如下处理：
    获取刷脸机具的风险数据；
    利用所述刷脸机具的风险数据判断交易的支付风险是否可控。
  19. 根据权利要求18所述的装置，其中，
    所述风险控制模块被配置为执行如下处理：
    获取M个维度的刷脸机具风险数据；其中，M为正整数；以及
    对每一个维度的刷脸机具风险数据均进行归一化处理，得到该维度的刷脸机具风险向量；
    则，所述利用所述刷脸机具的风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算机具风险值：
    Figure PCTCN2022070469-appb-100013
    其中，R d(X d)表征所述机具风险值，
    Figure PCTCN2022070469-appb-100014
    表征第m个维度的机具风险向量，
    Figure PCTCN2022070469-appb-100015
    的值为0或1，m为1至M中的任意一个整数；
    如果所述机具风险值为1，则确定所述交易的支付风险可控。
  20. 根据权利要求18所述的装置，其中，所述刷脸机具的风险数据包括如下中的任一项：所述刷脸机具的软件环境的风险数据、所述刷脸机具的硬件环境的风险数据以 及通信网络风险数据。
  21. 根据权利要求14所述的装置，其中，所述风险控制模块进一步被配置为执行如下处理：
    获取商户的风险数据；
    利用所述商户的风险数据判断交易的支付风险是否可控。
  22. 根据权利要求21所述的装置，其中，
    所述风险控制模块被配置为执行如下处理：
    获取I个维度的商户风险数据；其中，I为正整数；以及
    对每一个维度的商户风险数据均进行归一化处理，得到该维度的商户风险向量；
    则，所述利用所述商户的风险数据判断交易的支付风险是否可控，包括：
    利用如下计算式，计算商户风险值：
    Figure PCTCN2022070469-appb-100016
    其中
    Figure PCTCN2022070469-appb-100017
    常量b>1
    其中，R m(X m)表征所述商户风险值，
    Figure PCTCN2022070469-appb-100018
    表征第i个维度的商户风险向量，i为1至I中的任意一个整数；
    如果所述商户风险值大于第二预定值，则确定所述交易的支付风险可控。
  23. 根据权利要求21所述的装置，其中，所述商户的风险数据包括如下中的任一项：商户的历史行为数据、所述用户的信用状态数据、所述商户的服务等级数据。
  24. 根据权利要求14至23中任一所述的装置，其中，所述用户的风险数据包括如下中的任一项：所述用户的历史行为数据、所述用户的消费能力统计数据、所述用户的信用状态数据以及所述用户的芝麻信用分数。
  25. 根据权利要求14至23中任一所述的装置，进一步包括：扣款处理模块，被配置为执行如下处理中的至少一项：
    在所述风险控制模块判断出交易的支付风险可控之后，利用获取的所述用户的账户信息进行扣款处理，如果扣款不成功，则从预先设立的刷脸付资金池的账户中进行扣款；和/或，
    在所述风险控制模块判断出交易的支付风险不可控之后，利用获取的所述用户的账户信息进行扣款处理，如果扣款不成功，则通知用户扣款失败，如果扣款成功，则通知用户可离开。
  26. 一种计算机可读存储介质，其上存储有计算机程序，当所述计算机程序在计算机中执行时，令计算机执行权利要求1-13中任一项所述的方法。
  27. 一种计算设备，包括存储器和处理器，其特征在于，所述存储器中存储有可执行代码，所述处理器执行所述可执行代码时，实现权利要求1-13中任一项所述的方法。
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116051109A (zh) * 2022-12-29 2023-05-02 湖南安智网络科技有限公司 一种基于人脸识别的消费签约系统

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140046831A1 (en) * 2012-08-09 2014-02-13 Ncr Corporation Transaction system and method
CN106056380A (zh) * 2016-05-27 2016-10-26 深圳市雪球科技有限公司 移动支付风险控制系统以及移动支付风险控制方法
CN107481019A (zh) * 2017-07-28 2017-12-15 上海携程商务有限公司 订单欺诈识别方法、系统、存储介质和电子设备
CN109919692A (zh) * 2019-03-11 2019-06-21 上海高顿新文化传媒有限公司 一种智能店
CN112734437A (zh) * 2021-01-11 2021-04-30 支付宝(杭州)信息技术有限公司 刷脸支付的方法和装置

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7416112B2 (en) * 2006-04-05 2008-08-26 Diebold Self-Service Systems Division Of Diebold, Incorporated Automated banking machine system and method
US10510073B2 (en) * 2013-08-08 2019-12-17 Visa International Service Association Methods and systems for provisioning mobile devices with payment credentials
US10043184B2 (en) * 2014-05-30 2018-08-07 Paypal, Inc. Systems and methods for implementing transactions based on facial recognition
US20180308100A1 (en) * 2017-04-19 2018-10-25 Risto Haukioja System and method of client recognition for service provider transactions
US20180349912A1 (en) * 2017-06-06 2018-12-06 Eric M. Fiterman Authenticating and authorizing retail transactions using face and location data
CN208077240U (zh) * 2017-12-04 2018-11-09 阿里巴巴集团控股有限公司 实体场所中关联数据对象的确定系统
US11288646B2 (en) * 2018-12-12 2022-03-29 365 Retail Markets, Llc Platform-agnostic account access using facial recognition
CN109872160A (zh) * 2019-02-01 2019-06-11 广州逗号智能科技有限公司 电子支付方法及装置
CN110189132A (zh) * 2019-04-19 2019-08-30 北京百度网讯科技有限公司 人脸支付装置、方法、系统及机器可读存储介质
CN110570200B (zh) * 2019-08-16 2020-08-25 阿里巴巴集团控股有限公司 一种支付方法及装置
CN110491044A (zh) * 2019-08-19 2019-11-22 广州织点智能科技有限公司 一种刷脸支付方法、装置、电子设备及存储介质
CN111275428A (zh) * 2020-02-10 2020-06-12 支付宝(杭州)信息技术有限公司 信息处理方法、装置、设备和介质
CN111539711A (zh) * 2020-04-24 2020-08-14 支付宝(杭州)信息技术有限公司 一种安全业务交易方法、装置及电子设备
CN111461695A (zh) * 2020-06-19 2020-07-28 支付宝(杭州)信息技术有限公司 一种刷脸支付方法、装置及设备
CN112101901A (zh) * 2020-09-14 2020-12-18 北京嘀嘀无限科技发展有限公司 支付提醒方法、装置、系统、用户终端及服务器

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140046831A1 (en) * 2012-08-09 2014-02-13 Ncr Corporation Transaction system and method
CN106056380A (zh) * 2016-05-27 2016-10-26 深圳市雪球科技有限公司 移动支付风险控制系统以及移动支付风险控制方法
CN107481019A (zh) * 2017-07-28 2017-12-15 上海携程商务有限公司 订单欺诈识别方法、系统、存储介质和电子设备
CN109919692A (zh) * 2019-03-11 2019-06-21 上海高顿新文化传媒有限公司 一种智能店
CN112734437A (zh) * 2021-01-11 2021-04-30 支付宝(杭州)信息技术有限公司 刷脸支付的方法和装置

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116051109A (zh) * 2022-12-29 2023-05-02 湖南安智网络科技有限公司 一种基于人脸识别的消费签约系统

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