Payment method, apparatus, device, storage medium and product

Abstract

The application belongs to the technical field of data processing, and discloses a payment method, device, equipment, storage medium and product. The application collects near-infrared images and grayscale images of a user's fingerprint when receiving payment data sent by a payment device, then verifies the user's identity according to the near-infrared images and the grayscale images, and performs payment based on the payment data after the user's identity is verified. The application verifies the user's identity according to the near-infrared images and the grayscale images of the user's fingerprint when receiving the payment data sent by the payment device, combines the advantage that the near-infrared images can display clearer internal fingerprint structures penetrating the skin with the surface texture information of the grayscale images, obtains images more conducive to fingerprint feature point extraction, and then verifies the user's identity according to the images. Compared with a single verification mode, the above mode of the application can safely and effectively verify the user's identity, thereby enhancing the security of the payment process.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a payment method, apparatus, device, storage medium and product. Background Technology

[0002] With the widespread adoption of smartphones and the rapid development of mobile payments, consumers' demand for convenient payments is increasing. NFC (Near Field Communication) technology, due to its convenience and security, has been widely used in the mobile payment field. While existing payment systems are convenient and fast, they still have some security vulnerabilities, such as personal information leakage and payment fraud. Current NFC-based secure payment systems transmit data wirelessly, making them more vulnerable to hacking and data theft compared to wired transmission. Hackers may use specialized equipment to read and intercept sensitive information in NFC communications, such as bank card information and ID card numbers, or use devices carrying malicious software or specially designed NFC tags to carry out fraudulent transactions or attacks, masquerading as legitimate payment terminals or access control systems to deceive users and obtain sensitive information. Therefore, how to conduct payments securely and effectively has become an urgent problem to be solved. Summary of the Invention

[0003] The main purpose of this application is to provide a payment method, device, equipment, storage medium, and product, which aims to solve the technical problem of how to make payments securely and effectively.

[0004] To achieve the above objectives, this application provides a payment method, which includes the following steps:

[0005] Upon receiving payment data from the payment device, near-infrared and grayscale images of the user's fingerprints are collected.

[0006] The user's identity is verified based on the near-infrared image and the grayscale image;

[0007] After the user's identity is verified, payment is made based on the payment data.

[0008] Optionally, the step of verifying the user's identity based on the near-infrared image and the grayscale image specifically includes:

[0009] Image registration is performed on the near-infrared image and the grayscale image to obtain a registered near-infrared image and a registered grayscale image;

[0010] The registered near-infrared image and the registered grayscale image are fused to obtain a fused image.

[0011] Biometric verification of users is performed based on the fused image;

[0012] If biometric verification fails, the user's identity will be verified based on the password entered by the user.

[0013] Optionally, the step of performing image registration on the near-infrared image and the grayscale image to obtain a registered near-infrared image and a registered grayscale image specifically includes:

[0014] The near-infrared image and the grayscale image are subjected to image denoising processing respectively to obtain a denoised near-infrared image and a denoised grayscale image;

[0015] The near-infrared image and the grayscale image after denoising are extracted by the hash corner detection algorithm to obtain near-infrared corners and grayscale corners.

[0016] Based on the near-infrared corner points and the grayscale corner points, image registration is performed on the denoised near-infrared image and the denoised grayscale image to obtain the registered near-infrared image and the registered grayscale image.

[0017] Optionally, the step of fusing the registered near-infrared image and the registered grayscale image to obtain a fused image specifically includes:

[0018] The registered near-infrared image is subjected to wavelet decomposition to obtain near-infrared low-frequency sub-band, near-infrared horizontal high-frequency sub-band, near-infrared vertical high-frequency sub-band and near-infrared diagonal high-frequency sub-band.

[0019] The registered grayscale image is subjected to wavelet decomposition to obtain grayscale low-frequency sub-band, grayscale horizontal high-frequency sub-band, grayscale vertical high-frequency sub-band, and grayscale diagonal high-frequency sub-band.

[0020] The near-infrared low-frequency sub-band and the grayscale low-frequency sub-band are fused to obtain a fused low-frequency sub-band.

[0021] The near-infrared horizontal high-frequency sub-band and the grayscale horizontal high-frequency sub-band are fused to obtain a fused horizontal high-frequency sub-band.

[0022] The near-infrared vertical high-frequency sub-band and the grayscale vertical high-frequency sub-band are fused to obtain a fused vertical high-frequency sub-band.

[0023] The near-infrared diagonal high-frequency subband and the grayscale diagonal high-frequency subband are fused to obtain a fused diagonal high-frequency subband.

[0024] The fused low-frequency subband, the fused horizontal high-frequency subband, the fused vertical high-frequency subband, and the fused diagonal high-frequency subband are subjected to small inverse wave transform processing to obtain the fused image.

[0025] Optionally, before the step of collecting the near-infrared image and grayscale image of the user's fingerprint upon receiving payment data sent by the payment device, the method further includes:

[0026] Electromagnetic signals are collected when the payment device sends payment data;

[0027] The electromagnetic signal is converted into a digital signal to obtain a discrete signal sequence;

[0028] Calculate the electromagnetic interference intensity based on the discrete signal sequence;

[0029] The output power of the NFC chip is adjusted according to the intensity of electromagnetic interference, and the payment data is transmitted through the adjusted NFC chip.

[0030] Optionally, the step of making payment based on the payment data after user authentication is successful specifically includes:

[0031] After the user's identity is verified, a payment order is generated based on the payment data;

[0032] Feature extraction is performed on the near-infrared corner points, and a preset key factor is determined based on the extracted feature vectors;

[0033] The target key is obtained by synthesizing the random key factor and the preset key factor;

[0034] The payment order is encrypted using the target key, and payment is made based on the encrypted payment order.

[0035] Furthermore, to achieve the above objectives, this application also provides a payment device, the payment device comprising:

[0036] The image acquisition module is used to acquire near-infrared and grayscale images of the user's fingerprint when receiving payment data sent by the payment device;

[0037] An identity verification module is used to verify the user's identity based on the near-infrared image and the grayscale image;

[0038] The payment module is used to make payments based on the payment data after the user's identity verification is successful.

[0039] In addition, to achieve the above objectives, this application also proposes a payment device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the payment method as described above.

[0040] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the payment method described above.

[0041] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the payment method described above.

[0042] This application acquires near-infrared and grayscale images of a user's fingerprint upon receiving payment data from a payment device. The user's identity is then verified based on these images, and payment is processed based on the payment data after successful authentication. This application verifies user identity by combining the advantage of near-infrared images (which can penetrate skin and clearly reveal the internal fingerprint structure) with the surface texture information of grayscale images, resulting in an image more conducive to fingerprint feature point extraction. This image is then used to verify user identity. Compared to single verification methods, this method securely and effectively verifies user identity and processes payment based on the payment data, thereby enhancing the security of the payment process. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the first embodiment of the payment method of this application;

[0046] Figure 2 This is a flowchart illustrating the second embodiment of the payment method of this application;

[0047] Figure 3 This is a structural block diagram of the first embodiment of the payment device of this application;

[0048] Figure 4 This is a schematic diagram of the structure of a payment device in the hardware operating environment involved in the embodiments of this application.

[0049] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0051] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solution of this application embodiment is: when receiving payment data sent by the payment device, collecting near-infrared image and grayscale image of the user's fingerprint; verifying the user's identity based on the near-infrared image and the grayscale image; and making payment based on the payment data after the user's identity verification is successful.

[0053] With the widespread adoption of smartphones and the rapid development of mobile payments, consumers' demand for convenient payments is increasing. NFC (Near Field Communication) technology, due to its convenience and security, has been widely used in the mobile payment field. While existing payment systems are convenient and fast, they still have some security vulnerabilities, such as personal information leakage and payment fraud. Existing NFC-based secure payment systems transmit data in a wireless environment, which, compared to wired transmission, makes them more vulnerable to hacking and data theft. Hackers may use specialized equipment to read and intercept sensitive information in NFC communications, such as bank card information and ID card numbers, or use devices carrying malicious software or specially designed NFC tags to carry out fraudulent transactions or attacks, masquerading as legitimate payment terminals or access control systems to deceive users and obtain sensitive information.

[0054] This application acquires near-infrared and grayscale images of a user's fingerprint upon receiving payment data from a payment device. The user's identity is then verified based on these images, and payment is processed based on the payment data after successful authentication. This application verifies user identity by combining the advantage of near-infrared images (which can penetrate skin and clearly reveal the internal fingerprint structure) with the surface texture information of grayscale images, resulting in an image more conducive to fingerprint feature point extraction. This image is then used to verify user identity. Compared to single verification methods, this method securely and effectively verifies user identity and processes payment based on the payment data, thereby enhancing the security of the payment process.

[0055] It should be noted that the executing entity of this application can be a payment server. A payment server is a special server used to process electronic payment transactions. It acts as a bridge between merchants and payment gateways, effectively connecting all aspects of the payment transaction and ensuring the secure transmission of data and the smooth completion of the transaction.

[0056] Based on this, the embodiments of this application provide a payment method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the payment method of this application.

[0057] In this embodiment, the payment method includes the following steps:

[0058] Step S10: Upon receiving payment data sent by the payment device, collect near-infrared and grayscale images of the user's fingerprint.

[0059] Understandably, the payment device can be a merchant-side device. When the payment server receives payment data sent by the payment device, the payment data may include the payment amount, payment time, etc. At this time, near-infrared images and grayscale images corresponding to the user's fingerprint can be acquired through optical and capacitive sensors. Grayscale images can well show the surface texture information of the fingerprint, such as the grayscale changes of fingerprint ridges and valleys, the thickness and direction of the lines, etc. Near-infrared images focus on penetrating the skin surface to reveal the internal fingerprint structure.

[0060] Furthermore, in this embodiment, before step S10, the method further includes: acquiring electromagnetic signals when the payment device sends payment data; converting the electromagnetic signals into digital signals to obtain a discrete signal sequence; calculating the electromagnetic interference intensity based on the discrete signal sequence; adjusting the output power of the NFC chip based on the electromagnetic interference intensity; and transmitting the payment data through the adjusted NFC chip.

[0061] It should be understood that this embodiment can realize short-range wireless communication between the payment device and the receiving device through the NFC communication module, which is a key component for realizing short-range wireless communication between the payment device and the receiving device. The NFC communication module includes a sensor unit, a signal processing unit, and a transmission power adjustment unit. The sensor unit includes an electromagnetic sensor, which is used to sample the electromagnetic signals of the environment around the payment device (based on the electromagnetic signals generated by the payment bill issued by the merchant-side device) and transmit the collected electromagnetic signals to the signal processing unit. The signal processing unit uses an analog-to-digital converter to convert the analog signals into digital signals to obtain a discrete signal sequence x[n], where n = 0, 1, 2, ..., N-1, and N is the number of sampling points. The electromagnetic interference intensity R is calculated using the following formula.

[0062]

[0063] In a specific implementation, the transmission power adjustment unit can adjust the output power of the NFC chip according to the electromagnetic interference intensity. Specifically, threshold values R1 and R2 are set. When R < R1, the electromagnetic interference intensity is low, and there is no need to adjust the output power of the NFC chip. When R1 ≤ R ≤ R2, the electromagnetic interference intensity is medium, and the output power of the NFC chip increases by 10% - 25%. When R ≥ R2, the electromagnetic interference intensity is high, and the output power of the NFC chip increases by 25% - 50%. By adjusting the output power of the NFC chip, the stable transmission of NFC signals in a complex electromagnetic environment is ensured. Compared with a system without this adaptive adjustment function, it can effectively avoid data loss or errors caused by electromagnetic interference, thus significantly improving the payment success rate. In some commercial places with complex electromagnetic environments (such as near electronic device sales areas, large communication base stations, etc.), using an ordinary NFC payment system may frequently result in payment failures, while the system of this embodiment can adaptively adjust to ensure the smooth transmission of payment data by the adjusted NFC chip.

[0064] Step S20: Verify the user identity based on the near-infrared image and the grayscale image.

[0065] It can be understood that in this embodiment, the user identity can be verified based on the near-infrared image and the grayscale image. Specifically, the near-infrared image and the grayscale image can be first fused, combining the advantage of the near-infrared image that can penetrate the skin to show a clearer internal fingerprint structure with the texture information of the grayscale image to obtain an image that is more conducive to the extraction of fingerprint feature points. Then, the biometric features of the user are identified based on this image, and combined with multiple identity verifications such as password verification, so as to more accurately and effectively verify the user identity.

[0066] Step S30: After the user identity verification is passed, perform payment based on the payment data.

[0067] It should be understood that after the user identity verification is passed, the payment server can perform payment based on the payment data. Specifically, the payment can be performed through a secure transaction module. The secure transaction processing module includes an encryption unit, a verification unit, and a confirmation unit. The encryption unit uses a symmetric encryption algorithm to encrypt the payment data. It uses the same key for encryption and decryption. At the same time, the encryption unit is also responsible for the generation, storage, and management of the key. The key is generated based on a secure random number generator and stored using a secure hardware security module HSM to ensure that the key is not leaked. Before the payment data is transmitted, the encryption unit encrypts it to generate encrypted payment information. Even if the encrypted payment information is stolen, the original payment information cannot be directly parsed, thus ensuring the security of the payment information.

[0068] In its implementation, the verification unit verifies the digital certificate and digital signature of the receiving device to ensure the legitimacy of the payment request. After a successful transaction, the confirmation unit generates transaction confirmation information, which typically includes the transaction amount, transaction time, and the identity information of both parties. Finally, the generated transaction confirmation information is sent to the user and the receiving device through a secure communication channel. The user can check the transaction confirmation information to confirm whether the transaction was successful and save it as a transaction credential. The receiving device can use the transaction confirmation information to update the transaction status and perform subsequent processing.

[0069] This embodiment acquires near-infrared and grayscale images of the user's fingerprint upon receiving payment data from the payment device. The user's identity is then verified based on these images. After successful user authentication, payment is processed based on the payment data. This embodiment verifies user identity by combining the advantage of near-infrared images (which can penetrate skin and clearly reveal the internal fingerprint structure) with the surface texture information of grayscale images, resulting in an image more conducive to fingerprint feature point extraction. This image is then used to verify user identity. Compared to single verification methods, this embodiment provides a more secure and effective way to verify user identity and process payments based on the payment data, thereby enhancing the security of the payment process.

[0070] refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the payment method of this application.

[0071] Based on the first embodiment described above, in this embodiment, step S20 includes:

[0072] Step S201: Perform image registration on the near-infrared image and the grayscale image to obtain the registered near-infrared image and the registered grayscale image.

[0073] Understandably, due to differences in position and angle between near-infrared and grayscale images, image registration is required to obtain registered near-infrared and grayscale images.

[0074] Furthermore, in order to effectively register the near-infrared image and the grayscale image, in this embodiment, step S201 includes: performing image denoising processing on the near-infrared image and the grayscale image respectively to obtain a denoised near-infrared image and a denoised grayscale image; extracting corner points from the denoised near-infrared image and the denoised grayscale image respectively using a hash corner detection algorithm to obtain near-infrared corner points and grayscale corner points; and performing image registration on the denoised near-infrared image and the denoised grayscale image based on the near-infrared corner points and the grayscale corner points to obtain a registered near-infrared image and a registered grayscale image.

[0075] It should be understood that image denoising can be performed on the near-infrared image and the grayscale image separately to improve image quality and obtain denoised near-infrared image and denoised grayscale image.

[0076] Understandably, for near-infrared images T N Near-infrared corner points (x, y) are extracted using a hash corner detection algorithm. The local autocorrelation matrix M of the near-infrared image at each pixel (x, y) is calculated, i.e.:

[0077]

[0078] Among them, T x and T y It is a near-infrared image T N (x,y) represents the partial derivatives in the x and y directions, and w represents a local window (the target fingerprint feature in the near-infrared image has a certain scale, and the window size can be selected according to this scale. For example, if the target fingerprint is about 10 pixels, in order to better capture the surrounding local autocorrelation, the window size can be selected to be about 15×15 pixels, which can ensure that the window can cover the target fingerprint and part of the surrounding area, thereby effectively calculating the local autocorrelation matrix). Next, the corner response function R is calculated, with the formula: R(x,y)=det(M)-k(trace(M)) 2 Where det(M) is the determinant of matrix M, trace(M) is the trace of matrix M, k is an empirical constant, and a threshold R is set. i At that time, R(x,y)>R i The point is the near-infrared corner point. Similarly, for the grayscale image T... G The same corner detection operation is performed on (x,y) to obtain the gray-scale corners corresponding to the gray-scale image.

[0079] In the specific implementation, the near-infrared corner points corresponding to the denoised near-infrared image and the gray-scale corner points corresponding to the denoised gray-scale image are matched. The denoised near-infrared image is transformed to the same coordinate space as the denoised gray-scale image, so that the denoised near-infrared image and the denoised gray-scale image are aligned in spatial position, resulting in the registered near-infrared image and the registered gray-scale image.

[0080] Step S202: Perform image fusion on the registered near-infrared image and the registered grayscale image to obtain the fused image.

[0081] Further, in this embodiment, step S202 includes: performing wavelet decomposition processing on the registered near-infrared image to obtain near-infrared low-frequency sub-bands, near-infrared horizontal high-frequency sub-bands, near-infrared vertical high-frequency sub-bands, and near-infrared diagonal high-frequency sub-bands; performing wavelet decomposition processing on the registered grayscale image to obtain grayscale low-frequency sub-bands, grayscale horizontal high-frequency sub-bands, grayscale vertical high-frequency sub-bands, and grayscale diagonal high-frequency sub-bands; fusing the near-infrared low-frequency sub-bands and the grayscale low-frequency sub-bands to obtain fused low-frequency sub-bands; and performing wavelet decomposition processing on the near-infrared horizontal high-frequency sub-bands to obtain near-infrared horizontal high-frequency sub-bands. The high-frequency subband and the gray-level horizontal high-frequency subband are fused to obtain a fused horizontal high-frequency subband; the near-infrared vertical high-frequency subband and the gray-level vertical high-frequency subband are fused to obtain a fused vertical high-frequency subband; the near-infrared diagonal high-frequency subband and the gray-level diagonal high-frequency subband are fused to obtain a fused diagonal high-frequency subband; the fused low-frequency subband, the fused horizontal high-frequency subband, the fused vertical high-frequency subband, and the fused diagonal high-frequency subband are subjected to a small inverse wave transform to obtain a fused image.

[0082] Understandably, a scaling function can be used. The wavelet function ψ(x) is used to register the near-infrared image T. N Wavelet decomposition is performed on (x,y) to obtain the near-infrared low-frequency subband (LL), near-infrared horizontal high-frequency subband (LH), near-infrared vertical high-frequency subband (HL), and near-infrared diagonal high-frequency subband (HH), as shown in the formula:

[0083]

[0084] Where m is the scale parameter and n is the displacement parameter; the scale parameter m determines the level of image decomposition, that is, the level of detail in the image. If a coarser image feature is desired, a larger value of m will be chosen. The displacement parameter n is used to locate features on the image plane; these two values ​​are fixed parameters. Similarly, wavelet decomposition can be performed on the registered grayscale image using the above formula to obtain grayscale low-frequency subbands, grayscale horizontal high-frequency subbands, grayscale vertical high-frequency subbands, and grayscale diagonal high-frequency subbands.

[0085] It should be understood that the near-infrared low-frequency sub-band and the grayscale low-frequency sub-band can be fused to obtain a fused low-frequency sub-band. Specifically, a weighted average is used to fuse the low-frequency sub-bands. The calculation formula for the fused low-frequency sub-band is derived from the previous decomposition using scaling and wavelet functions according to the corresponding formulas. First, the low-frequency sub-bands of the near-infrared image and the grayscale image must be accurately decomposed using the above formulas before they can be fused according to the weighted average rule to obtain the final low-frequency sub-band used for subsequent inverse wavelet transform. The fused low-frequency sub-band LL F (x, y) can be LL F (x, y) = w1 × LL N (x, y) + w2×LL G (x, y), where w1 and w2 are weighting coefficients (these coefficients can be determined based on image quality and specific application requirements), and w1 + w2 = 1, LL N (x, y) represents the near-infrared low-frequency subband, LL G (x, y) represents the grayscale low-frequency subband.

[0086] In the specific implementation, the horizontal, vertical, and diagonal high-frequency subbands all employ a fusion method based on calculating local region energy. When calculating local region energy, whether for near-infrared or grayscale images, the calculations are performed on the corresponding high-frequency subbands after decomposition. The pixel data involved in the formula for calculating local region energy corresponds to the high-frequency subbands obtained through the initial four scaling functions and wavelet function decomposition formulas. Furthermore, the adaptive selection of the window size is to better fuse these decomposed high-frequency subbands based on their local characteristics such as texture variations, thereby obtaining the fused high-frequency subbands and providing accurate input subband data for the final inverse wavelet transform.

[0087] In the horizontal high-frequency sub-band, a regional energy fusion method is used to calculate the local regional energy E at (x,y) of the near-infrared image and the grayscale image. LH,N(x,y) and E LH,G(x,y) The calculation formulas are as follows:

[0088]

[0089] Where k is a local window, and the process of obtaining this local window is as follows:

[0090] First, the local gradient information of the fingerprint image is calculated to measure the degree of texture change. For the registered near-infrared image T... N (x,y) and the registered grayscale image T G For (x, y), the Sobel operator can be used to compute the gradients G in the horizontal and vertical directions. x,TG y,T G x,G And. Then G y,G Calculate the gradient magnitude M T and M G The calculation formula is:

[0091]

[0092] The window size is determined based on the gradient magnitude. A threshold for the gradient magnitude is set; if it exceeds the threshold (indicating drastic texture changes), the window size k is set to 3×3; if it is less than the threshold, k is set to 5×5. This adaptive method can better adapt to the local characteristics of different fingerprint images; the fused horizontal high-frequency subband LH F (x, y) is:

[0093]

[0094] The vertical high-frequency subband and diagonal high-frequency subband are fused using the same method as the horizontal high-frequency subband to obtain the fused vertical high-frequency subband HL. F (x, y) is:

[0095]

[0096] The fused diagonal high-frequency subband HH F (x, y) is:

[0097]

[0098] In the specific implementation, a small inverse wave transform is performed on the fused low-frequency subband, the fused horizontal high-frequency subband, the fused vertical high-frequency subband, and the fused diagonal high-frequency subband to obtain the fused image T. F (x,y). By fusing near-infrared (NIR) and grayscale images, the advantage of NIR images—which can penetrate the skin and reveal the internal fingerprint structure more clearly—is combined with the texture information of grayscale images to obtain an image more conducive to fingerprint feature point extraction. Grayscale images can well display the surface texture information of fingerprints, such as the grayscale variations of fingerprint ridges and valleys, the thickness and direction of the lines, etc. NIR images, on the other hand, focus on penetrating the skin surface to reveal the internal fingerprint structure. Fusing the two can achieve complementarity between structural and texture information. For example, when the fingerprint surface is worn or stained, causing the texture to be blurred, NIR images can provide relatively clear structural information of the underlying layer, while the texture details of grayscale images can further supplement and refine these structures, so that the fused image can more completely present the overall appearance of the fingerprint.

[0099] Step S203: Perform biometric verification on the user based on the fused image.

[0100] Understandably, the fused image is compared with the fingerprint information stored in the database to determine whether the two match, thereby enabling biometric verification of the user.

[0101] Step S204: If biometric verification fails, verify the user's identity based on the password entered by the user.

[0102] It should be understood that when biometric verification fails, the user can be prompted to enter a preset password to verify their identity. The user's identity can be verified by comparing the entered password with a pre-stored password. Password verification uses the SHA-256 hash algorithm to encrypt the entered password. The formula is: H = textSHA-256(P), where P represents the entered password and H represents the calculated hash value. After obtaining the hash value, it is compared with the hash value stored in the database. If the two match, the verification is successful; otherwise, the verification fails.

[0103] Further, in this embodiment, step S30 includes: after the user's identity verification is passed, generating a payment order based on the payment data; extracting features from the near-infrared corner points and determining a preset key factor based on the extracted feature vectors; synthesizing a target key by combining a random key factor and the preset key factor; encrypting the payment order based on the target key and making payment based on the encrypted payment order.

[0104] Understandably, after user authentication, a payment order can be generated based on payment data, and the payment order is dynamically encrypted through a dynamic key generation module. The dynamic key generation module is a key component of the secure payment system; it is responsible for generating a unique and unpredictable key for each transaction to ensure transaction security. Specifically, a series of random values ​​can be generated using a blockchain network. These random values ​​serve as key material, possessing unpredictability and uniqueness, and ensuring that the generated random numbers have sufficient randomness and entropy to resist various cryptanalysis attacks. First, a blockchain network is randomly selected, and the number of blocks n to be selected is determined based on the requirements for randomness and security of the random numbers. Within the selected blockchain network, the entire node is run, and through the node's API, the hash value of each selected block is obtained. The hash value of each block is stored in a local temporary data structure, and the stored hash value sequence is: H = [h1, h2, ..., h...]. n ], where h n This represents the hash value of the nth selected block. The hash values ​​are normalized to a fixed length, ensuring that each hash value has the same length. The normalized hash values ​​are then concatenated sequentially into a long string: S = h1‖h2‖...‖h nS is hashed again using a standard hash function SHA-256, with the formula: R = Hash(S). The result R is used as the generated random number, i.e., the random key factor. Each block in the blockchain contains a large amount of transaction information and hash values, which have strong randomness and unpredictability.

[0105] It should be understood that the key synthesis unit combines a random key factor with a preset key factor to generate a unique key. The preset key factor is the feature vector of the fingerprint corner points collected by the biometric identification unit. Feature extraction is performed on the near-infrared corner points to obtain a fingerprint image of size W×H, with the corner point coordinates being (x, y). The corner point positions are then quantized using the following formula:

[0106]

[0107] in, Indicates rounding down, N x and N y These two values ​​are preset values ​​for the location quantization level.

[0108] Set up quantization for n corner points. For each corner point i (i = 1, 2, ..., n), there is a quantized value x. qi and y qi quantized value x qi and y qi By concatenating them into a vector in a fixed order, a subvector V is obtained. i =[x qi ,y qi ], to all subvectors V i When concatenated, they form a complete feature vector of the corner point: V F =[V1,V2,...,V n Next, the key synthesis unit uses the hash function SHA-256 to combine the random key factor and the preset key factor, first combining the random key factor R and the preset key factor V. F The concatenation is used as the input to the hash function, i.e., Input = (R, V) F Next, a 256-bit hash value is calculated using SHA-256. This hash value can then be used as the target key for generation. The formula is: K ′ =SHA-256(R,V) F This key generation method ensures that a new, unique key is generated each time fingerprint information is combined with different dynamic key factors, which is used for subsequent authentication or data encryption operations. Finally, the payment order is encrypted based on the target key, and payment is made based on the encrypted payment order.

[0109] In its implementation, the transaction record module ensures the integrity, searchability, and security of transaction information. When a transaction occurs, the system stores the relevant information in a cloud storage service within the transaction information storage unit. Data is encrypted during storage to ensure the security of the transaction information. Simultaneously, the transaction information query unit provides a web-based user interface, allowing users and administrators to search for matching transaction records within the transaction information storage unit based on input query criteria, and then displaying these records to the users and administrators. The transaction record module also includes a transaction information backup unit, which periodically copies data from the transaction information storage unit to another cloud storage service to create a backup. In the event of a failure of the primary storage medium or data loss, the system can restore transaction information from the backup.

[0110] In addition, the anomaly detection and alarm module is designed to monitor abnormal situations in the transaction process in real time and trigger alarm mechanisms and processing procedures when an anomaly is detected. The anomaly detection unit identifies abnormal behavior by monitoring key parameters in the transaction process in real time. When the anomaly detection unit detects an anomaly, the alarm triggering unit will immediately activate the alarm mechanism and send alarm information to the user in the form of SMS and email push notifications to remind the user to pay attention to transaction security. The alarm triggering will also record anomaly logs for subsequent analysis and investigation.

[0111] Furthermore, it may include a payment limit management module for setting user payment limits and blocking transactions and alerting users when they attempt to make payments exceeding those limits. It also includes a device status monitoring module for real-time monitoring of the payment and receiving devices' status, such as battery level and NFC communication quality, and alerting users and administrators when device status is abnormal. Finally, it includes a remote update module for remotely updating the software and firmware of the payment and receiving devices to ensure system security and continuous functionality updates.

[0112] This embodiment registers a near-infrared image and a grayscale image to obtain registered near-infrared and grayscale images. Then, it fuses these two images to obtain a fused image. The fused image is then used to perform biometric verification on the user. If biometric verification fails, the user's identity is verified using their entered password. This embodiment combines the advantages of near-infrared images (which can penetrate skin and clearly display the internal fingerprint structure) with the texture information of grayscale images to obtain an image more conducive to fingerprint feature point extraction. The fused image then accurately and effectively performs biometric verification on the user. If biometric verification fails, the user's identity is verified using their entered password, thereby improving the security of user authentication.

[0113] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the payment device of this application.

[0114] like Figure 3 As shown, the payment device proposed in this application includes:

[0115] The image acquisition module 10 is used to acquire near-infrared and grayscale images of the user's fingerprint when receiving payment data sent by the payment device;

[0116] The identity verification module 20 is used to verify the user's identity based on the near-infrared image and the grayscale image;

[0117] The payment module 30 is used to make payments based on the payment data after the user's identity verification is successful.

[0118] This embodiment acquires near-infrared and grayscale images of the user's fingerprint upon receiving payment data from the payment device. The user's identity is then verified based on these images. After successful user authentication, payment is processed based on the payment data. This embodiment verifies user identity by combining the advantage of near-infrared images (which can penetrate skin and clearly reveal the internal fingerprint structure) with the surface texture information of grayscale images, resulting in an image more conducive to fingerprint feature point extraction. This image is then used to verify user identity. Compared to single verification methods, this embodiment provides a more secure and effective way to verify user identity and process payments based on the payment data, thereby enhancing the security of the payment process.

[0119] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0120] In addition, for technical details not described in detail in this embodiment, please refer to the payment method provided in any embodiment of this application, which will not be repeated here.

[0121] Based on the first embodiment of the payment device described in this application, a second embodiment of the payment device of this application is proposed.

[0122] In this embodiment, the authentication module 20 is further configured to perform image registration on the near-infrared image and the grayscale image to obtain a registered near-infrared image and a registered grayscale image; perform image fusion on the registered near-infrared image and the registered grayscale image to obtain a fused image; perform biometric verification on the user based on the fused image; and verify the user's identity based on the password entered by the user when biometric verification fails.

[0123] Furthermore, the authentication module 20 is also used to perform image denoising processing on the near-infrared image and the grayscale image respectively to obtain a denoised near-infrared image and a denoised grayscale image; to extract corner points on the denoised near-infrared image and the denoised grayscale image respectively using a hash corner detection algorithm to obtain near-infrared corner points and grayscale corner points; and to perform image registration on the denoised near-infrared image and the denoised grayscale image based on the near-infrared corner points and the grayscale corner points to obtain a registered near-infrared image and a registered grayscale image.

[0124] Furthermore, the authentication module 20 is also used to perform wavelet decomposition processing on the registered near-infrared image to obtain near-infrared low-frequency sub-bands, near-infrared horizontal high-frequency sub-bands, near-infrared vertical high-frequency sub-bands, and near-infrared diagonal high-frequency sub-bands; to perform wavelet decomposition processing on the registered grayscale image to obtain grayscale low-frequency sub-bands, grayscale horizontal high-frequency sub-bands, grayscale vertical high-frequency sub-bands, and grayscale diagonal high-frequency sub-bands; to fuse the near-infrared low-frequency sub-bands and the grayscale low-frequency sub-bands to obtain fused low-frequency sub-bands; and to perform wavelet decomposition processing on the near-infrared horizontal high-frequency sub-bands. The sub-band and the grayscale horizontal high-frequency sub-band are fused to obtain a fused horizontal high-frequency sub-band; the near-infrared vertical high-frequency sub-band and the grayscale vertical high-frequency sub-band are fused to obtain a fused vertical high-frequency sub-band; the near-infrared diagonal high-frequency sub-band and the grayscale diagonal high-frequency sub-band are fused to obtain a fused diagonal high-frequency sub-band; the fused low-frequency sub-band, the fused horizontal high-frequency sub-band, the fused vertical high-frequency sub-band, and the fused diagonal high-frequency sub-band are subjected to a small inverse wave transform to obtain a fused image.

[0125] Furthermore, the image acquisition module 10 is also used to acquire electromagnetic signals when the payment device sends payment data; convert the electromagnetic signals into digital signals to obtain a discrete signal sequence; calculate the electromagnetic interference intensity based on the discrete signal sequence; adjust the output power of the NFC chip based on the electromagnetic interference intensity; and transmit the payment data through the adjusted NFC chip.

[0126] Furthermore, the payment module 30 is also used to generate a payment order based on the payment data after the user's identity verification is passed; to extract features from the near-infrared corner points and determine a preset key factor based on the extracted feature vector; to synthesize a target key by combining a random key factor and the preset key factor; to encrypt the payment order based on the target key; and to make payment based on the encrypted payment order.

[0127] Other embodiments or specific implementations of the payment device of this application can be found in the above-described method embodiments, and will not be repeated here.

[0128] This application provides a payment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the payment method in Embodiment 1 above.

[0129] The following is for reference. Figure 4 The diagram illustrates a structural schematic of a payment device suitable for implementing embodiments of this application. The payment device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The payment device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0130] like Figure 4As shown, the payment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the payment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the payment device to communicate wirelessly or wiredly with other devices to exchange data. Although payment devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0131] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0132] The payment device provided in this application, employing the payment method described in the above embodiments, can solve the technical problem of how to conduct payments securely and effectively. Compared with the prior art, the beneficial effects of the payment device provided in this application are the same as those of the payment method provided in the above embodiments, and other technical features of the payment device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0133] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0134] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0135] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the payment method in the above embodiments.

[0136] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0137] The aforementioned computer-readable storage medium may be included in the payment device or may exist independently without being assembled into the payment device.

[0138] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a payment device, cause the payment device to: upon receiving payment data sent by the payment device, acquire near-infrared and grayscale images of the user's fingerprint; verify the user's identity based on the near-infrared and grayscale images; and, after successful user authentication, make payment based on the payment data.

[0139] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0141] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0142] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described payment method, thereby solving the technical problem of how to conduct payments securely and efficiently. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the payment method provided in the above embodiments, and will not be repeated here.

[0143] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the payment method described above.

[0144] The computer program product provided in this application can solve the technical problem of how to conduct payments securely and efficiently. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the payment method provided in the above embodiments, and will not be repeated here.

[0145] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A payment method, characterized in that, The payment method includes the following steps: Upon receiving payment data from the payment device, near-infrared and grayscale images of the user's fingerprints are collected. The user's identity is verified based on the near-infrared image and the grayscale image; After the user's identity is verified, payment is made based on the payment data.

2. The payment method as described in claim 1, characterized in that, The step of verifying the user's identity based on the near-infrared image and the grayscale image specifically includes: Image registration is performed on the near-infrared image and the grayscale image to obtain a registered near-infrared image and a registered grayscale image; The registered near-infrared image and the registered grayscale image are fused to obtain a fused image. Biometric verification of users is performed based on the fused image; If biometric verification fails, the user's identity will be verified based on the password entered by the user.

3. The payment method as described in claim 2, characterized in that, The step of performing image registration on the near-infrared image and the grayscale image to obtain the registered near-infrared image and the registered grayscale image specifically includes: The near-infrared image and the grayscale image are subjected to image denoising processing respectively to obtain a denoised near-infrared image and a denoised grayscale image; The near-infrared image and the grayscale image after denoising are extracted by the hash corner detection algorithm to obtain near-infrared corners and grayscale corners. Based on the near-infrared corner points and the grayscale corner points, image registration is performed on the denoised near-infrared image and the denoised grayscale image to obtain the registered near-infrared image and the registered grayscale image.

4. The payment method as described in claim 2, characterized in that, The step of fusing the registered near-infrared image and the registered grayscale image to obtain the fused image specifically includes: The registered near-infrared image is subjected to wavelet decomposition to obtain near-infrared low-frequency sub-band, near-infrared horizontal high-frequency sub-band, near-infrared vertical high-frequency sub-band and near-infrared diagonal high-frequency sub-band. The registered grayscale image is subjected to wavelet decomposition to obtain grayscale low-frequency sub-band, grayscale horizontal high-frequency sub-band, grayscale vertical high-frequency sub-band, and grayscale diagonal high-frequency sub-band. The near-infrared low-frequency sub-band and the grayscale low-frequency sub-band are fused to obtain a fused low-frequency sub-band. The near-infrared horizontal high-frequency sub-band and the grayscale horizontal high-frequency sub-band are fused to obtain a fused horizontal high-frequency sub-band. The near-infrared vertical high-frequency sub-band and the grayscale vertical high-frequency sub-band are fused to obtain a fused vertical high-frequency sub-band. The near-infrared diagonal high-frequency subband and the grayscale diagonal high-frequency subband are fused to obtain a fused diagonal high-frequency subband. The fused low-frequency subband, the fused horizontal high-frequency subband, the fused vertical high-frequency subband, and the fused diagonal high-frequency subband are subjected to small inverse wave transform processing to obtain the fused image.

5. The payment method as described in claim 1, characterized in that, Before the step of collecting near-infrared and grayscale images of the user's fingerprint upon receiving payment data from the payment device, the method further includes: Electromagnetic signals are collected when the payment device sends payment data; The electromagnetic signal is converted into a digital signal to obtain a discrete signal sequence; Calculate the electromagnetic interference intensity based on the discrete signal sequence; The output power of the NFC chip is adjusted according to the intensity of electromagnetic interference, and the payment data is transmitted through the adjusted NFC chip.

6. The payment method as described in claim 3, characterized in that, The step of making payment based on the payment data after user authentication is successful specifically includes: After the user's identity is verified, a payment order is generated based on the payment data; Feature extraction is performed on the near-infrared corner points, and a preset key factor is determined based on the extracted feature vectors; The target key is obtained by synthesizing the random key factor and the preset key factor; The payment order is encrypted using the target key, and payment is made based on the encrypted payment order.

7. A payment device, characterized in that, The payment device includes: The image acquisition module is used to acquire near-infrared and grayscale images of the user's fingerprint when receiving payment data sent by the payment device; An identity verification module is used to verify the user's identity based on the near-infrared image and the grayscale image; The payment module is used to make payments based on the payment data after the user's identity verification is successful.

8. A payment device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the payment method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the payment method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the payment method as described in any one of claims 1 to 6.

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