Payment information processing method for identifying user payment intention and apparatus therefor

Abstract

The present application relates to a kind of payment information processing method and its device for identifying user payment intention.The method includes: in response to the request for issuing of mobile acquirer equipment, generate target trajectory feature sequence for display to user;Receive multiple first feature points constituting first movement to obtain first trajectory feature sequence, wherein first movement is the movement of the biological characteristics of user to the target trajectory feature sequence implemented;Receive multiple second feature points constituting second movement to obtain second trajectory feature sequence, wherein second movement is the movement of mobile acquirer equipment to the target trajectory feature sequence implemented;And based on the first similarity of first trajectory feature sequence and target trajectory feature sequence and the second similarity of second trajectory feature sequence and target trajectory feature sequence, the authenticity of user payment intention is judged.According to the present application, the authenticity of user payment intention can be effectively identified, and the risk of theft and brush is prevented.

Description

Technical Field

[0001] This invention relates to payment information processing technology, and more specifically to a payment information processing method and apparatus for identifying a user's payment intention. Background Technology

[0002] Currently, the existing technologies for facial recognition payment mainly include the following two mainstream solutions:

[0003] Option (1): Payment is made through "face recognition + mobile phone number". In some common scenarios, it is also possible to achieve operation without entering a mobile phone number. However, in reality, face recognition and mobile phone number are only used to confirm the account. They are still different from the traditional payment process, cannot reflect the user's intention, have weak transaction verification strength, and pose certain security risks.

[0004] Option (2): The "FacePay" product uses a "face + payment password" verification method. The payment password reflects the payment intention and is more secure than Option (1). However, it requires users to increase their memory burden and the speed of entering the payment password is slower, which greatly reduces the payment experience. In addition, the additional PIN input and protection devices increase the implementation and maintenance costs for institutional promotion.

[0005] As mentioned above, the "face recognition + mobile phone number" method of scheme (1) cannot reflect the user's willingness to pay and poses a significant security risk. It is generally only suitable for fixed device scenarios.

[0006] The solution (2) "face recognition + payment password" can reflect the user's wishes, but it requires the user to increase the memory cost, and the introduction of payment password will increase the equipment cost and security maintenance cost. Summary of the Invention

[0007] In view of the above problems, the present invention aims to provide a payment information processing method and apparatus for identifying the authenticity of a user's payment intention without increasing equipment costs and memory costs.

[0008] A payment information processing method for identifying a user's payment intention according to one aspect of the present invention is characterized by comprising:

[0009] The trajectory generation step, in response to the acquiring request issued by the mobile acquiring device, generates a target trajectory feature sequence to be displayed to the user;

[0010] The first trajectory acquisition step involves receiving a plurality of first feature points constituting a first movement to obtain a first trajectory feature sequence, wherein the first movement is a movement performed by the user's biometrics in response to the target trajectory feature sequence.

[0011] The second trajectory acquisition step involves receiving a plurality of second feature points constituting the second movement to obtain a second trajectory feature sequence, wherein the second movement is a movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and

[0012] The trajectory determination step determines the authenticity of the user's payment intention based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence.

[0013] Optionally, in the trajectory determination step, it is determined whether the first similarity and the second similarity satisfy predetermined conditions respectively.

[0014] Optionally, in the trajectory determination step, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met, the user's willingness to pay is determined to be genuine; otherwise, the user's willingness to pay is determined to be non-genuine.

[0015] Optionally, in the trajectory generation step, in response to a payment request issued by the mobile acquiring device, a random function generator is used to generate the target trajectory feature sequence.

[0016] Optionally, the target trajectory feature sequence includes: a starting position, an ending position, and the trajectory between the starting position and the ending position.

[0017] Optionally, the first trajectory acquisition step includes:

[0018] The first receiving sub-step involves receiving a plurality of first feature points constituting the first movement;

[0019] The first judgment sub-step involves determining whether the first distance between the first feature point and the termination position is less than a preset predetermined distance. If it is less than the predetermined distance, the detection of user movement continues; otherwise, the detection stops.

[0020] The first acquisition sub-step involves obtaining a first trajectory feature sequence based on multiple first feature points.

[0021] The second trajectory acquisition step includes:

[0022] The second receiving sub-step involves acquiring a plurality of second feature points constituting the second movement; and

[0023] The second judgment sub-step involves determining whether the second distance between the second feature point and the termination position is less than a preset predetermined distance. If it is less than the predetermined distance, the movement of the mobile acquiring device continues to be detected; otherwise, the detection stops.

[0024] The second acquisition sub-step involves obtaining the second trajectory feature sequence based on multiple second feature points.

[0025] Optionally, the second trajectory acquisition step includes:

[0026] Receive multiple second feature points constituting the second movement implemented by the mobile acquiring device in response to the target trajectory feature sequence;

[0027] The intermediate trajectory feature sequence is obtained based on multiple second feature points; and

[0028] The intermediate trajectory feature sequence is transformed into a coordinate space, so as to transform it from the image display coordinate space of the target trajectory feature sequence to the mobile acquiring device coordinate space where the mobile acquiring device itself does not move. The intermediate trajectory feature sequence after the coordinate space transformation is identified as the second trajectory feature sequence.

[0029] Optionally, the user's biometrics include any one of the following: face, palm print, palm vein, and iris.

[0030] Optionally, multiple second feature points of the second movement performed by the mobile acquiring device in response to the target trajectory feature sequence are obtained by receiving motion sensor signals from the mobile acquiring device.

[0031] Optionally, the motion sensor signal includes at least one of an accelerometer signal, a gyroscope signal, and a magnetometer signal.

[0032] Optionally, the first trajectory acquisition step further includes:

[0033] The first timeout judgment sub-step determines whether the detection time of the first movement performed by the detection user against the target trajectory feature sequence exceeds a preset time limit.

[0034] The second trajectory acquisition step further includes:

[0035] The second timeout judgment sub-step detects whether the detection time of the second movement implemented by the mobile acquiring device for the target trajectory feature sequence exceeds a preset time limit.

[0036] A payment information processing method for identifying a user's payment intention according to one aspect of the present invention is characterized by comprising:

[0037] The request is sent out by issuing a payment request.

[0038] The receiving and display step involves receiving and displaying the target trajectory feature sequence generated based on the acquiring request;

[0039] The first trajectory detection step involves detecting multiple first feature points representing the movement of the user's biometric features against the target trajectory feature sequence.

[0040] The second trajectory detection step involves detecting multiple second feature points of movement performed by the mobile acquiring device in response to the target trajectory feature sequence; and

[0041] The result receiving step receives the result of the user's payment intention authenticity judgment based on the first feature point and the second feature point.

[0042] Optionally, in the second trajectory detection step, multiple second feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence are obtained by detecting motion sensor signals.

[0043] Optionally, the motion sensor signal includes at least one of an accelerometer signal, a gyroscope signal, and a magnetometer signal.

[0044] A payment information processing apparatus for identifying a user's payment intention, as described in one aspect of the present invention, is characterized by comprising:

[0045] The trajectory generation module is used to generate a target trajectory feature sequence to be displayed to the user in response to a payment request issued by the mobile payment acquiring device.

[0046] The first trajectory acquisition module is used to receive a plurality of first feature points constituting a first movement to obtain a first trajectory feature sequence, wherein the first movement is a movement performed by the user's biometrics in response to the target trajectory feature sequence;

[0047] The second trajectory acquisition module is configured to receive a plurality of second feature points constituting a second movement to obtain a second trajectory feature sequence, wherein the second movement is a movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and

[0048] The trajectory determination module is used to determine the authenticity of a user's willingness to pay based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence.

[0049] Optionally, in the trajectory determination module, it is determined whether the first similarity and the second similarity satisfy predetermined conditions respectively.

[0050] Optionally, in the trajectory judgment module, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met, the user's willingness to pay is determined to be genuine; otherwise, the user's willingness to pay is determined to be non-genuine.

[0051] Optionally, in the trajectory generation module, in response to a payment request issued by the mobile payment acquiring device, a random function generator is used to generate the target trajectory feature sequence.

[0052] Optionally, the first trajectory acquisition module includes:

[0053] A first receiving submodule is configured to receive a plurality of first feature points constituting the first movement; and

[0054] The first acquisition submodule is used to acquire a first trajectory feature sequence based on multiple first feature points.

[0055] The second trajectory acquisition module includes:

[0056] The second receiving submodule is used to receive multiple second feature points constituting the second movement implemented by the mobile acquiring device in response to the target trajectory feature sequence;

[0057] The second acquisition submodule is used to obtain an intermediate trajectory feature sequence based on multiple second feature points; and

[0058] The conversion submodule is used to perform coordinate space conversion on the intermediate trajectory feature sequence, so as to convert the image display coordinate space of the target trajectory feature sequence into the mobile acquiring device coordinate space where the mobile acquiring device itself does not move, and to identify the intermediate trajectory feature sequence after coordinate space conversion as the second trajectory feature sequence.

[0059] Optionally, it further includes:

[0060] The timeout judgment module is used to determine whether the detection time of the first movement performed by the detection user on the target trajectory feature sequence and the detection time of the second movement performed by the detection mobile acquiring device on the target trajectory feature sequence exceed a preset time limit.

[0061] A mobile payment acquiring device according to one aspect of the present invention is characterized in that it comprises:

[0062] The request sending module is used to send payment collection requests;

[0063] The display module is used to receive and display the target trajectory feature sequence generated based on the acquiring request;

[0064] The first trajectory detection module is used to detect multiple first feature points of movement of the user's biometric features in response to the target trajectory feature sequence;

[0065] The second trajectory detection module is used to detect multiple second feature points of movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and

[0066] The result receiving module is used to receive the result of the judgment on the authenticity of the user's payment intention based on the first feature point and the second feature point.

[0067] Optionally, the second trajectory detection module obtains multiple second feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence by detecting motion sensor signals.

[0068] Optionally, the motion sensor signal includes at least one of an accelerometer signal, a gyroscope signal, and a magnetometer signal.

[0069] The computer-readable medium of the present invention stores a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the above-described payment information processing method for identifying a user's intention to pay.

[0070] The computer device of the present invention includes a storage module, a processor, and a computer program stored on the storage module and executable on the processor, characterized in that the processor, when executing the computer program, implements the above-described payment information processing method for identifying a user's payment intention.

[0071] The payment information processing method and apparatus of the present invention for identifying a user's intention to pay can quickly and accurately identify whether a user has a genuine intention to pay, and can detect and prevent fraudulent activities that use the relative movement of mobile acquiring devices to represent a user's genuine intention to pay. Attached Figure Description

[0072] Figure 1 This is a system framework diagram illustrating an example of a payment information processing system for identifying a user's willingness to pay, according to the present invention.

[0073] Figure 2 This is a flowchart illustrating an example of a payment information processing method for identifying a user's willingness to pay, according to the present invention.

[0074] Figure 3 This is a schematic diagram illustrating the payment process in which the present invention's user payment intention recognition process has been added to the facial recognition payment method.

[0075] Figure 4 This is a flowchart illustrating a payment information processing method for identifying a user's willingness to pay, according to one embodiment of the present invention.

[0076] Figure 5 This is a system framework diagram illustrating a payment information processing apparatus for identifying a user's willingness to pay, as an example of the present invention.

[0077] Figure 6This is a system framework diagram illustrating another example of the present invention: a payment information processing apparatus for identifying a user's willingness to pay. Detailed Implementation

[0078] The following are some embodiments of the present invention, intended to provide a basic understanding of the invention. They are not intended to identify key or decisive elements of the invention or to limit the scope of protection sought.

[0079] For purposes of brevity and illustrativeness, the principles of the invention are described herein primarily with reference to exemplary embodiments thereof. However, those skilled in the art will readily recognize that the same principles are equivalently applicable to all types of payment information processing methods and apparatuses for identifying a user's intention to pay, and that these same principles can be implemented therein, and that any such variations do not depart from the true spirit and scope of this patent application.

[0080] Furthermore, reference is made in the following description to the accompanying drawings, which illustrate specific exemplary embodiments. Electrical, mechanical, logical, and structural modifications may be made to these embodiments without departing from the spirit and scope of the invention. Moreover, while features of the invention are disclosed in conjunction with only one of several embodiments, this feature may be combined with one or more other features of other embodiments if desired and / or advantageous for any given or identifiable function. Therefore, the following description should not be considered limiting in any sense, and the scope of the invention is defined by the appended claims and their equivalents.

[0081] Terms such as “possessing” and “comprising” indicate that, in addition to having units (modules) and modules that are directly and explicitly stated in the specification and claims, the technical solution of the present invention does not exclude the presence of other units (modules) and modules that are not directly or explicitly stated.

[0082] Figure 1 This is a system framework diagram illustrating an example of a payment information processing system for identifying a user's willingness to pay, according to the present invention. Figure 1 As shown, the payment information processing system for identifying a user's payment intention according to the present invention includes a back-end server 100 and a mobile acquiring device 200. The mobile acquiring device 200 can be a point-of-sale (POS) device or a mobile terminal with POS functionality.

[0083] First, a payment information processing method for identifying a user's payment intention will be described as one aspect of the present invention. This payment information processing method can be applied to... Figure 1 The backend server is 100.

[0084] Figure 2This is a flowchart illustrating an example of a payment information processing method for identifying a user's willingness to pay, according to the present invention.

[0085] like Figure 2 As shown, an example of the payment information processing method for identifying a user's willingness to pay according to the present invention includes the following steps:

[0086] Trajectory generation step S100: In response to the acquiring request issued by the mobile acquiring device 200, a target trajectory feature sequence is generated to be displayed to the user;

[0087] First trajectory acquisition step S200: Receive multiple first feature points from the mobile acquiring device 200 representing the first movement performed by the biometrics of the user in relation to the target trajectory feature sequence, to acquire the first trajectory feature sequence;

[0088] Second trajectory acquisition step S300: The mobile acquiring device 200 receives a plurality of second feature points constituting the second movement performed by the mobile acquiring device against the target trajectory feature sequence, to acquire the second trajectory feature sequence; and

[0089] Trajectory determination step S400: Based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence, the authenticity of the user's payment intention is determined.

[0090] In the trajectory determination step S400, it is determined whether the first similarity and the second similarity satisfy predetermined conditions. Specifically, in the trajectory determination step S400, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met simultaneously, the user's payment intention is determined to be genuine; otherwise, the user's payment intention is determined to be insincere.

[0091] In the trajectory generation step S100, in response to the payment request issued by the mobile acquiring device 200, the target trajectory feature sequence is generated using a random function generator. This effectively prevents third parties from stealing or imitating the target trajectory feature sequence.

[0092] The target trajectory feature sequence includes: the starting position, the ending position, and the trajectory between the starting position and the ending position.

[0093] As an example, the first trajectory acquisition step S200 includes:

[0094] The first receiving sub-step involves receiving a plurality of first feature points constituting the first movement performed by the user in response to the target trajectory feature sequence.

[0095] The first judgment sub-step (optional step) determines whether the first distance between the first feature point and the termination position is less than a preset predetermined distance. If it is less than the predetermined distance, the detection of user movement continues; otherwise, the detection stops.

[0096] The first acquisition sub-step involves obtaining the first trajectory feature sequence based on multiple first feature points.

[0097] As an example, the second trajectory acquisition step S300 includes:

[0098] The second receiving sub-step involves acquiring multiple second feature points constituting the movement performed by the mobile acquiring device against the target trajectory feature sequence; and

[0099] The second judgment sub-step (optional step) determines whether the second distance between the second feature point and the termination position is less than a preset predetermined distance. If it is less than the predetermined distance, the movement of the mobile acquiring device is detected; otherwise, the detection is stopped.

[0100] The second acquisition sub-step involves obtaining the second trajectory feature sequence based on multiple second feature points.

[0101] As another example, the second trajectory acquisition step S300 includes:

[0102] Receive multiple second feature points constituting the second movement implemented by the mobile acquiring device in response to the target trajectory feature sequence;

[0103] The intermediate trajectory feature sequence is obtained based on multiple second feature points; and

[0104] The intermediate trajectory feature sequence is transformed into a coordinate space, so as to transform it from the image display coordinate space of the target trajectory feature sequence to the mobile acquiring device coordinate space where the mobile acquiring device itself does not move. The intermediate trajectory feature sequence after the coordinate space transformation is identified as the second trajectory feature sequence.

[0105] In this invention, by setting the first trajectory acquisition step S200, it is possible to obtain the user's biometrics and the first movement implemented against the target trajectory feature sequence, and to obtain the first trajectory feature sequence. By comparing the first similarity between the first trajectory feature sequence and the target trajectory feature sequence with a first threshold, if the first similarity is less than the first threshold, it is actually possible to identify the user's willingness to pay.

[0106] In this invention, to further increase the accuracy of identifying a user's willingness to pay, a second trajectory feature sequence is obtained, and a comparison of a second similarity and a second threshold is added. The reason for this is to prevent the user who is not expressing the willingness to pay from moving along the target trajectory feature sequence, but rather the mobile acquiring device moving along the target trajectory feature sequence. Since the movement of the mobile acquiring device along the target trajectory feature sequence is a mirror image of the user's movement, a coordinate space transformation is first performed before comparing it with the second threshold. If the value is less than the second threshold, it is considered that the mobile acquiring device is not moving.

[0107] The execution order of the first trajectory acquisition step S200 and the second trajectory acquisition step S300 can be arbitrary or simultaneous, and this is not limited in this invention.

[0108] As another example, a second movement trajectory of the mobile acquiring device in response to the target trajectory feature sequence is obtained by receiving motion sensor signals from the mobile acquiring device. Here, the motion sensor signals include at least one of accelerometer signals, gyroscope signals, and magnetometer signals.

[0109] In this invention, the biometric features of a user include any one of the following: face, palm print, palm veins, and iris, etc.

[0110] Next, a payment information processing method for identifying a user's payment intention will be described as another aspect of the present invention. This payment information processing method can be applied to... Figure 1 200 mobile acquiring devices in China.

[0111] Another aspect of the present invention provides a payment information processing method for identifying a user's willingness to pay, comprising:

[0112] The request is sent out by issuing a payment request.

[0113] The receiving and display step involves receiving and displaying the target trajectory feature sequence generated based on the acquiring request;

[0114] The first trajectory detection step involves detecting multiple first feature points representing the movement of the user's biometric features against the target trajectory feature sequence.

[0115] The second trajectory detection step involves detecting multiple second feature points of movement performed by the mobile acquiring device in response to the target trajectory feature sequence; and

[0116] The result receiving step receives the result of the user's payment intention authenticity judgment based on the first feature point and the second feature point.

[0117] In the second trajectory detection step, multiple first feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence are obtained by detecting motion sensor signals.

[0118] As an example, motion sensor signals include at least one of accelerometer signals, gyroscope signals, and magnetometer signals.

[0119] Next, a specific embodiment of the payment information processing method for identifying a user's intention to pay according to the present invention will be described. In this embodiment, a human face will be used as an example as a user's biometric feature.

[0120] The core of facial recognition payment is facial recognition technology. Current facial recognition mainly involves processes such as image acquisition, face detection, facial image quality assessment, liveness detection, facial feature extraction, and facial feature matching. While liveness detection effectively defends against common attacks such as photos, face swapping, masks, occlusion, and screen capture, it is ineffective against "remote payment" attacks. The root cause of "remote payment" lies in the lack of payment intent verification. Furthermore, facial information is one of the few publicly available, contactless biometric information sources; due to this unique characteristic, payment intent verification becomes even more crucial.

[0121] Therefore, this invention proposes a payment information processing method for identifying a user's willingness to pay and introduces a payment information processing device for identifying a user's willingness to pay (this payment information processing device can also be called a "payment willingness identification module").

[0122] Figure 3 This is a schematic diagram illustrating the payment process in which the present invention's user payment intention recognition process has been added to the facial recognition payment method.

[0123] A brief introduction Figure 3 The payment process is shown below. Figure 3 The payment process shown includes:

[0124] Step S1: Start the process;

[0125] Step S2: Acquire images;

[0126] Step S3: Perform face detection;

[0127] Step S4: Determine if it is a human face. If the determination is no (N), return to step S2. If the determination is yes (Y), continue to step S5.

[0128] Step S5: Evaluate the quality of the face image;

[0129] Step S6: Determine whether the image quality is acceptable. If the determination is no (N), return to step S2. If the determination is yes (Y), continue to step S7.

[0130] Step S7: Perform liveness detection (using existing liveness detection technology);

[0131] Step S8: Determine if the body is alive. If the determination is no (N), skip to step S14. If the determination is yes (Y), continue to step S9.

[0132] Step S9: Identify willingness to pay;

[0133] Step S10: Determine whether it is the person's own will. If the determination is no (N), skip to step S14. If the determination is yes (Y), continue to step S11.

[0134] Step S11: Perform facial feature extraction and matching;

[0135] Step S12: Determine whether the account matching is successful. If the determination is no (N), skip to step S14. If the determination is yes (Y), continue to step S13.

[0136] Step S13: Process the payment (e.g., deduct payment);

[0137] Step S14: Perform exception handling;

[0138] Step S15: End the process.

[0139] The payment information processing flow for identifying a user's payment intention in this invention mainly involves... Figure 3 Steps S9 and S10 of the present invention will be described below as a payment information processing flow for identifying a user's intention to pay, according to one embodiment of the present invention.

[0140] Figure 4 This is a flowchart illustrating a payment information processing method for identifying a user's willingness to pay, according to one embodiment of the present invention.

[0141] like Figure 4 As shown, a payment information processing method for identifying a user's willingness to pay, according to one embodiment of the present invention, includes:

[0142] Step S21: Start the process;

[0143] Step S22: Generate a random function using, for example, a random function generator;

[0144] Step S23: Generate a target trajectory feature sequence using a random function, wherein the target trajectory feature sequence includes the starting position, the ending position, and the trajectory, where the starting position, the ending position, and the trajectory are the position coordinates of the screen display image coordinate system;

[0145] Step S24: The mobile acquiring device displays the starting location, ending location, and trajectory to the user (cardholder), i.e., displays the target trajectory feature sequence S. r ;

[0146] Step S25: Begin detecting the movement of the face and the movement of the mobile acquiring device;

[0147] Step S26 (which can actually be understood as being included in step S25): In step S25, the distance R between the feature points of the face and the termination position is continuously detected. When R is greater than the threshold R g If the condition is met, then continue with steps S27 and S28 to continuously track and record the face, as well as detect and record the gyroscope and accelerometer of the mobile payment receiving device (face recognition device), until R is less than the threshold R. g When the detection stops and step S29 continues, in the above "detecting the distance R between the feature points of the face and the termination position", the "feature points of the face" compared with the termination position can be the average coordinate center of multiple feature points of the face.

[0148] Step S29: Determine if the detection process has timed out. If it has not timed out, continue with steps S30 and S32; otherwise, skip to step 36.

[0149] Step S30: Perform trajectory recognition of the target in the face image;

[0150] Step S31: Perform trajectory identification of the mobile acquiring device;

[0151] Step S32: Calculate the first trajectory feature sequence S1 and the target feature sequence S, which represent the trajectory of facial feature points. r The degree of similarity between them (i.e., the first similarity A);

[0152] Step S33: Convert the tracked device trajectory S2 (i.e., the intermediate trajectory feature sequence S2) into the equivalent target motion second trajectory feature sequence S′2, and calculate the representations of the second trajectory feature sequence S′2 and the target feature sequence S. r The second similarity B between them;

[0153] Step S34: Determine whether the first similarity A is greater than the first threshold G1 and whether the second similarity B is less than the second threshold G2. If both conditions are met, continue to step S35; otherwise, skip to step S36.

[0154] Step S35: Output result: Normal, which means that the intention to pay is genuine;

[0155] Step S36: Abnormal situation;

[0156] Step S37: End the process.

[0157] In the detection process of step S25, some further detailed requirements can be set: for example, the face must be facing the screen directly and of appropriate size; if the left or right tilt angle α is greater than a specified threshold, the detection fails. If a tilt angle α exists, a trajectory feature sequence mapped onto the camera plane of the mobile acquiring device is calculated based on the tilt angle α.

[0158] An example of the specific method for calculating similarity in step S32 is shown below:

[0159] For the first feature sequence S1 and the target feature sequence S r The similarity A is calculated, for example using the following cosine similarity:

[0160]

[0161] Where S1 represents the first feature sequence, S r Let A represent the target feature sequence, and let cosθ represent the similarity A.

[0162] In step S33, before converting the intermediate trajectory feature sequence S2 into the second trajectory feature sequence S′2 of the equivalent target motion, the identified trajectory of the mobile acquiring device is initially a three-dimensional sequence feature, which is referred to here as the initial sequence feature. However, the target feature sequence S, which serves as the basis for comparison r Since it is a two-dimensional sequence feature, in order to compare the two, in step S33, the actual initial sequence features collected must first be compared. A mapping transformation is performed to convert it into a two-dimensional intermediate trajectory feature sequence S2.

[0163] As an example of a calculation method, the motion trajectory of a three-dimensional mobile acquiring device is shown here. The intermediate trajectory feature sequence S2 is transformed into a two-dimensional sequence using the following formula:

[0164]

[0165] Where α is the left and right tilt angle.

[0166] Here, the above assumes that the vertical angle of the face is positive, meaning there is no significant upward or downward gaze. That is, the pitch angle γ = 0. Therefore, no projection calculation is performed in this dimension. Only the left and right tilt angle α is calculated. The calculation principle is the same for both dimensions. Furthermore, it is assumed that the distance d from the face to the camera plane will not change significantly during the acquisition process. One method is to monitor the interpupillary distance (IPD). For example, if the IPD is greater than or less than a threshold, it indicates a detection failure. Another method could be to estimate the change in distance d using image AI algorithms and IPD, and then calculate the position of the mapped point. If multiple sets of feature points are averaged, and α = 0 and γ = 0, then theoretically, although the position of an individual feature point changes, the average coordinate value remains unchanged.

[0167] In step S33, the intermediate trajectory feature sequence S2 needs to be converted into the second trajectory feature sequence S′2 of the equivalent target motion because the movement of the mobile acquiring device along the target trajectory feature sequence is a mirror image of the user's movement, so a coordinate space transformation is required. Here, an algorithmic method for converting the intermediate trajectory feature sequence S2 into the second trajectory feature sequence S′2 of the equivalent target motion with the camera position stationary is, for example, the following coordinate transformation:

[0168] The intermediate trajectory feature sequence S2 is as follows:

[0169]

[0170] The second trajectory feature sequence of the equivalent target motion can be obtained by S′2=T×S2, where T is the coordinate system transformation matrix, and T is:

[0171]

[0172] Then, after coordinate transformation, the second trajectory feature sequence S′2 is the following sequence:

[0173]

[0174] Therefore, the equivalent second trajectory feature sequence S′2 can be calculated.

[0175] Next, the second trajectory feature sequence S′2 and the target feature sequence S are calculated. r The second similarity B between them is calculated using the same method as the first similarity A calculated in step S31.

[0176] In step S34, it is necessary to determine whether the first similarity A is greater than the first threshold G1 and whether the second similarity B is less than the second threshold G2. The reasons for these two aspects are as follows:

[0177] To prevent the movement of the mobile acquiring device along the target trajectory feature sequence from being actually the user's movement that does not represent the user's intention to pay, and since the movement of the mobile acquiring device along the target trajectory feature sequence is a mirror image of the user's movement, a coordinate space transformation is first performed to obtain a second similarity. This second similarity is then compared to a second threshold. If the similarity is less than the second threshold, it is considered that the mobile acquiring device has not actively moved. This prevents the mobile acquiring device from impersonating the active intention of the mobile terminal by making relative movements.

[0178] The payment information processing method for identifying a user's willingness to pay has been described above. The payment information processing apparatus for identifying a user's willingness to pay according to the present invention will be described below.

[0179] Figure 5 This is a system framework diagram illustrating a payment information processing apparatus for identifying a user's willingness to pay, as an example of the present invention.

[0180] Figure 5 The payment information processing device shown can be applied to identify a user's willingness to pay. Figure 1 In the background server 100 shown, it is also referred to as 100 here.

[0181] like Figure 5 As shown, an example of the payment information processing apparatus 100 for identifying a user's intention to pay according to the present invention includes:

[0182] The trajectory generation module 110 is used to generate a target trajectory feature sequence to be displayed to the user in response to a payment request issued by the mobile payment acquiring device.

[0183] The first trajectory acquisition module 120 is used to receive a plurality of first feature points constituting a first movement to obtain a first trajectory feature sequence, wherein the first movement is a movement performed by the user's biometrics in response to the target trajectory feature sequence;

[0184] The second trajectory acquisition module 130 is configured to receive a plurality of second feature points constituting a second movement to obtain a second trajectory feature sequence, wherein the second movement is a movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and

[0185] The trajectory judgment module 140 is used to judge the authenticity of the user's payment intention based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence.

[0186] In the trajectory generation module 110, in response to the payment request issued by the mobile payment receiving device, the target trajectory feature sequence is generated using a random function generator.

[0187] Specifically, in the trajectory determination module 140, it is determined whether the first similarity and the second similarity satisfy predetermined conditions. In particular, in the trajectory determination module 400, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met simultaneously, the user's payment intention is determined to be genuine; otherwise, the user's payment intention is determined to be insincere.

[0188] Optionally, the first trajectory acquisition module 120 includes (not shown):

[0189] A first receiving submodule is configured to receive a plurality of first feature points constituting the first movement; and

[0190] The first acquisition submodule is used to acquire the first trajectory feature sequence based on multiple first feature points.

[0191] The second trajectory acquisition module 130 includes (not shown):

[0192] The second receiving submodule is configured to receive a plurality of second feature points constituting the second movement performed by the mobile acquiring device in response to the target trajectory feature sequence; and

[0193] The second acquisition submodule is used to obtain an intermediate trajectory feature sequence based on multiple second feature points; and

[0194] The conversion submodule is used to perform coordinate space conversion on the intermediate trajectory feature sequence, so as to convert the image display coordinate space of the target trajectory feature sequence into the mobile acquiring device coordinate space where the mobile acquiring device itself does not move, and to identify the intermediate trajectory feature sequence after coordinate space conversion as the second trajectory feature sequence.

[0195] As an optional embodiment, an example of the payment information processing device for identifying a user's willingness to pay further includes: a timeout judgment module 150, used to determine whether the detection time of the first movement performed by the user in relation to the target trajectory feature sequence and the detection time of the second movement performed by the mobile acquiring device in relation to the target trajectory feature sequence exceed a preset time.

[0196] Figure 6 This is a system framework diagram illustrating another example of the present invention: a payment information processing apparatus for identifying a user's willingness to pay. Figure 6 The payment information processing device shown can be applied to identify a user's willingness to pay. Figure 1The mobile acquiring device 200 shown here is also referred to as 200.

[0197] like Figure 6 As shown, another example of the payment information processing apparatus 200 for identifying a user's intention to pay includes:

[0198] The request issuing module 210 is used to issue a payment collection request;

[0199] Display module 220 is used to receive and display the target trajectory feature sequence generated based on the acquiring request;

[0200] The first trajectory detection module 230 is used to detect multiple first feature points of movement performed by the user's biometrics in response to the target trajectory feature sequence;

[0201] The second trajectory detection module 240 is used to detect multiple second feature points of movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and

[0202] The result receiving module 250 is used to receive the result of the judgment on the authenticity of the user's payment intention based on the first feature point and the second feature point.

[0203] The second trajectory detection module 240 obtains multiple second feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence by detecting motion sensor signals. Specifically, the motion sensor signals include at least one of accelerometer signals, gyroscope signals, and magnetometer signals.

[0204] The payment information processing method and apparatus of the present invention for identifying a user's intention to pay can quickly and accurately identify whether a user has a genuine intention to pay, and can detect and prevent fraudulent activities that use the relative movement of mobile acquiring devices to represent a user's genuine intention to pay.

[0205] The payment information processing method for identifying a user's payment intention of this invention can be applied to mobile facial recognition payment devices such as mobile POS and smart POS, as well as the backend of the payment system (i.e., the backend server). Using this invention, rapid identification of a user's payment intention can be achieved, effectively preventing abuse when the user has no intention to pay. When applied to facial recognition payment, it can prevent the risk of "remote fraud" in facial recognition payment.

[0206] The above examples primarily illustrate the payment information processing method and apparatus for identifying a user's intention to pay according to the present invention. Although only some specific embodiments of the invention have been described, those skilled in the art should understand that the invention can be implemented in many other forms without departing from its spirit and scope. Therefore, the examples and embodiments shown are considered illustrative rather than restrictive, and the invention may encompass various modifications and substitutions without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A payment information processing method for identifying a user's willingness to pay, characterized in that, include: The trajectory generation step, in response to the acquiring request issued by the mobile acquiring device, generates a target trajectory feature sequence to be displayed to the user; The first trajectory acquisition step involves receiving a plurality of first feature points constituting a first movement to obtain a first trajectory feature sequence, wherein the first movement is a movement performed by the user's biometrics in response to the target trajectory feature sequence. The second trajectory acquisition step involves receiving a plurality of second feature points constituting the second movement to obtain a second trajectory feature sequence, wherein the second movement is a movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and The trajectory determination step assesses the authenticity of the user's payment intention based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence. In the trajectory determination step, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met, the user's willingness to pay is determined to be genuine; otherwise, the user's willingness to pay is determined to be non-genuine.

2. The payment information processing method for identifying a user's willingness to pay as described in claim 1, characterized in that, In the trajectory generation step, in response to the acquiring request issued by the mobile acquiring device, the target trajectory feature sequence is generated using a random function generator.

3. The payment information processing method for identifying a user's willingness to pay as described in claim 1, characterized in that, The target trajectory feature sequence includes: a starting position, an ending position, and the trajectory between the starting position and the ending position.

4. The payment information processing method for identifying a user's willingness to pay as described in claim 3, characterized in that, The first trajectory acquisition step includes: The first receiving sub-step involves receiving a plurality of first feature points constituting the first movement; The first judgment sub-step involves determining whether the first distance between the first feature point and the termination position is greater than a preset predetermined distance. If it is greater than the predetermined distance, the detection of user movement continues; otherwise, the detection stops. The first acquisition sub-step involves obtaining a first trajectory feature sequence based on multiple first feature points. The second trajectory acquisition step includes: The second receiving sub-step involves acquiring a plurality of second feature points constituting the second movement; and The second judgment sub-step involves determining whether the second distance between the second feature point and the termination position is less than a preset predetermined distance. If it is less than the predetermined distance, the movement of the mobile acquiring device continues to be detected; otherwise, the detection stops. The second acquisition sub-step involves obtaining the second trajectory feature sequence based on multiple second feature points.

5. The payment information processing method for identifying a user's willingness to pay as described in claim 1, characterized in that, The second trajectory acquisition step includes: Receive multiple second feature points constituting the second movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; Multiple second feature points are mapped and transformed to obtain a two-dimensional intermediate trajectory feature sequence; and The intermediate trajectory feature sequence is transformed into a coordinate space, so as to transform it from the image display coordinate space of the target trajectory feature sequence to the mobile acquiring device coordinate space where the mobile acquiring device itself does not move. The intermediate trajectory feature sequence after the coordinate space transformation is identified as the second trajectory feature sequence.

6. The payment information processing method for identifying a user's willingness to pay as described in claim 1, characterized in that, The user's biometrics include any one of the following: face, palm print, palm veins, and iris.

7. The payment information processing method for identifying a user's willingness to pay as described in claim 1, characterized in that, Multiple second feature points are obtained by receiving motion sensor signals from the mobile acquiring device for the second movement performed by the mobile acquiring device in response to the target trajectory feature sequence.

8. The payment information processing method for identifying a user's willingness to pay as described in claim 7, characterized in that, The motion sensor signal includes at least one of accelerometer signal, gyroscope signal, and magnetometer signal.

9. The payment information processing method for identifying a user's willingness to pay as described in claim 4, characterized in that, The first trajectory acquisition step further includes: The first timeout judgment sub-step determines whether the detection time of the first movement performed by the detection user against the target trajectory feature sequence exceeds a preset time limit. The second trajectory acquisition step further includes: The second timeout judgment sub-step detects whether the detection time of the second movement implemented by the mobile acquiring device for the target trajectory feature sequence exceeds a preset time limit.

10. A payment information processing method for identifying a user's willingness to pay, characterized in that, include: The request is sent out by issuing a payment request. The receiving and display step involves receiving and displaying the target trajectory feature sequence generated based on the acquiring request; The first trajectory detection step involves detecting multiple first feature points representing the movement of the user's biometric features against the target trajectory feature sequence. The second trajectory detection step involves detecting multiple second feature points of movement performed by the mobile acquiring device in response to the target trajectory feature sequence. as well as The result receiving step receives the result of a judgment on the authenticity of the user's payment intention based on the first feature point and the second feature point. Specifically, a first trajectory feature sequence is obtained from multiple first feature points, and a second trajectory feature sequence is obtained from multiple second feature points. The authenticity of the user's payment intention is determined based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence. Specifically, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met, the user's payment intention is determined to be genuine; otherwise, the user's payment intention is determined to be non-genuine.

11. The payment information processing method for identifying a user's willingness to pay as described in claim 10, characterized in that, In the second trajectory detection step, multiple second feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence are obtained by detecting motion sensor signals.

12. The payment information processing method for identifying a user's willingness to pay as described in claim 11, characterized in that, The motion sensor signal includes at least one of accelerometer signal, gyroscope signal, and magnetometer signal.

13. A payment information processing device for identifying a user's intention to pay, characterized in that, include: The trajectory generation module is used to generate a target trajectory feature sequence to be displayed to the user in response to a payment request issued by the mobile payment acquiring device. The first trajectory acquisition module is used to receive a plurality of first feature points constituting a first movement to obtain a first trajectory feature sequence, wherein the first movement is a movement performed by the user's biometrics in response to the target trajectory feature sequence; The second trajectory acquisition module is configured to receive a plurality of second feature points constituting a second movement to obtain a second trajectory feature sequence, wherein the second movement is a movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; and The trajectory determination module is used to determine the authenticity of a user's payment intention based on a first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and a second similarity between the second trajectory feature sequence and the target trajectory feature sequence. Specifically, in the trajectory judgment module, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met simultaneously, the user's willingness to pay is determined to be genuine; otherwise, the user's willingness to pay is determined to be non-genuine.

14. The payment information processing apparatus for identifying a user's intention to pay as described in claim 13, characterized in that, In the trajectory generation module, in response to the payment request issued by the mobile payment receiving device, the target trajectory feature sequence is generated using a random function generator.

15. The payment information processing apparatus for identifying a user's intention to pay as described in claim 13, characterized in that, The first trajectory acquisition module includes: A first receiving submodule is configured to receive a plurality of first feature points constituting the first movement; and The first acquisition submodule is used to acquire a first trajectory feature sequence based on multiple first feature points. The second trajectory acquisition module includes: The second receiving submodule is used to receive multiple second feature points constituting the second movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; The second acquisition submodule is used to map and transform multiple second feature points to obtain a two-dimensional intermediate trajectory feature sequence; and The conversion submodule is used to perform coordinate space conversion on the intermediate trajectory feature sequence, so as to convert the image display coordinate space of the target trajectory feature sequence into the mobile acquiring device coordinate space where the mobile acquiring device itself does not move, and to identify the intermediate trajectory feature sequence after coordinate space conversion as the second trajectory feature sequence.

16. The payment information processing apparatus for identifying a user's intention to pay as described in claim 13, characterized in that, Further includes: The timeout judgment module is used to determine whether the detection time of the first movement performed by the user in response to the target trajectory feature sequence and the detection time of the second movement performed by the mobile acquiring device in response to the target trajectory feature sequence exceed a preset time limit.

17. A mobile payment acquiring device, characterized in that, include: The request sending module is used to send payment collection requests; The display module is used to receive and display the target trajectory feature sequence generated based on the acquiring request; The first trajectory detection module is used to detect multiple first feature points of movement of the user's biometric features in response to the target trajectory feature sequence; The second trajectory detection module is used to detect multiple second feature points of movement implemented by the mobile acquiring device in response to the target trajectory feature sequence; as well as The result receiving module is used to receive the result of the user's payment intention authenticity judgment based on the first feature point and the second feature point. Specifically, a first trajectory feature sequence is obtained from multiple first feature points, and a second trajectory feature sequence is obtained from multiple second feature points. The authenticity of the user's payment intention is determined based on the first similarity between the first trajectory feature sequence and the target trajectory feature sequence, and the second similarity between the second trajectory feature sequence and the target trajectory feature sequence. Specifically, it is determined whether the first similarity is greater than a preset first threshold and whether the second similarity is less than a preset second threshold. If both conditions are met, the user's payment intention is determined to be genuine; otherwise, the user's payment intention is determined to be non-genuine.

18. The mobile acquiring device as described in claim 17, characterized in that, The second trajectory detection module obtains multiple second feature points of the movement performed by the mobile acquiring device in response to the target trajectory feature sequence by detecting motion sensor signals.

19. The mobile acquiring device as described in claim 18, characterized in that, The motion sensor signal includes at least one of accelerometer signal, gyroscope signal, and magnetometer signal.

20. A computer-readable medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the payment information processing method for identifying a user's intention to pay, as described in any one of claims 1 to 9 or 10 to 12.

21. A computer device, comprising a storage module, a processor, and a computer program stored on the storage module and executable on the processor, characterized in that, When the processor executes the computer program, it implements the payment information processing method for identifying a user's payment intention as described in any one of claims 1 to 9 or 10 to 12.

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