Safe mobile payment processing system based on iris recognition

Abstract

The invention relates to the technical field of biological recognition, in particular to a safety mobile payment processing system based on iris recognition, which comprises an iris feature capture module, a feature correction and alignment module, a focus distance adjustment module, a user identity verification module, an identity matching algorithm module and a safety certification output module. According to the invention, by configuring the multi-angle optical image sensor and adjusting the acquisition distance, the adaptive capacity to users with different heights and the response speed of the system to environment change are enhanced, the image contrast is optimized and optical distortion is corrected by using the edge detection technology, and pixel-level image alignment is executed, so that the image problem caused by depth-of-field change is effectively solved; after the image capture quality is improved and the focus distance is adjusted, the accuracy and reliability of iris recognition are improved, and in secure mobile payment, the iris image is analyzed carefully and compared with database identity information, so that the security of identity verification is enhanced, unauthorized access and fraud are reduced, and the transaction legality and the user financial security are ensured.

Description

Technical Field

[0001] The present invention relates to the field of biometric identification technology, and in particular to a secure mobile payment processing system based on iris recognition. Background Art

[0002] The field of biometrics is a technology that uses human biological characteristics to identify individuals. Biological characteristics include fingerprints, irises, facial recognition, palm prints, voices, and DNA. The core advantage of biometrics lies in its uniqueness and difficulty in duplication, which makes it have extremely high application value in security verification and personal identification. This technology is widely used in security access control, mobile payments, law enforcement, and security management of personal devices. With the advancement of technology, the accuracy and adaptability of biometrics are continuously improving, making its application in daily life and business activities more and more popular.

[0003] Among them, the secure mobile payment processing system is a system that uses biometric technology to improve transaction security. The system verifies the legitimacy of transactions by verifying the user's biometric features (such as fingerprints or irises), thereby avoiding unauthorized access and fraud. The main purpose of this system is to simplify the payment process while improving its security, ensuring the information and financial security of users when making mobile payments. The secure mobile payment processing system can be widely used in e-commerce, retail point-of-sale and online banking, effectively improving the trust of the payment environment and the user's payment experience.

[0004] Existing technologies have certain limitations in terms of individual recognition accuracy and adaptability. Traditional biometric technology is limited by a single or low-flexibility sensor configuration and basic image processing methods, resulting in poor recognition results when the user's body shape and distance vary greatly. Existing technologies perform poorly in environments with complex light and varying depth of field, and are prone to misidentification, including in high-traffic public places such as shopping malls and transportation hubs. This technical limitation will directly affect the recognition speed and accuracy, thereby affecting the overall user experience and system security, and limiting the application of biometric technology in a wider range of scenarios, especially in mobile payment and access control systems that require high security and rapid response. The limitations of the technology lead to security vulnerabilities and increase the potential risks of the system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a secure mobile payment processing system based on iris recognition.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A secure mobile payment processing system based on iris recognition comprises:

[0007] The iris feature capture module configures a multi-angle optical image sensor on the iris recognition hardware to capture iris images with differentiated depth of field, adjusts the sensor to match the user's height, controls image exposure, and obtains iris image data;

[0008] The feature correction and alignment module uses edge detection to adjust image contrast, correct optical distortion, perform pixel-level image alignment, cope with differential depth of field changes, and generate a corrected iris image based on the iris image data;

[0009] The focus distance adjustment module analyzes the corrected iris image, matches the iris features of the differentiated depth of field, optimizes the image capture effect, and obtains the adjusted iris image;

[0010] The user identity verification module uses the adjusted iris image to analyze the iris features, perform basic identity verification that is not related to height, and compare the templates in the feature library to obtain a preliminary identity verification result;

[0011] The identity matching algorithm module performs feature analysis based on the preliminary identity authentication result, matches the iris pattern in the library, verifies identity consistency, and generates an identity matching result;

[0012] The security authentication output module uses the identity matching result to perform identity authentication, analyze the iris recognition hardware data, compare the identity information in the database, start the secure mobile payment processing, and generate security authentication information and payment verification information.

[0013] As a further solution of the present invention, the iris image data includes angle data and height matching index; the corrected iris image includes contrast enhancement results, distortion elimination effects, and alignment accuracy indexes; the adjusted iris image includes focal length adjustment value, depth of field adaptation, and image clarity; the preliminary identity authentication result includes an identity verification code, template matching degree, and verification success index; the identity matching result includes feature similarity, pattern consistency index, and matching verification value; the security authentication information and payment verification information include verification status, account status verification, and transaction activation signal.

[0014] As a further solution of the present invention, the iris feature capturing module includes:

[0015] The optical sensing submodule configures a multi-angle optical image sensor on the iris recognition hardware, uses angle adjustment and focal length optimization to capture iris original image data, and captures multi-level iris images and obtains iris optical data through the adjustment of the angle of the optical image sensor and the control of the focus.

[0016] The height matching submodule uses the iris optical data to adjust the collection distance of the optical image sensor according to the user's height information, takes continuous actions of measuring the height and adjusting the position of the optical image sensor, optimizes the angle and position of image capture, and obtains height-optimized image data;

[0017] The exposure control submodule optimizes the image data based on the height, optimizes the image quality by adjusting the aperture size, exposure time and ISO setting, controls the exposure parameters of the optical image sensor, matches the differentiated lighting conditions, and obtains the iris image data.

[0018] As a further solution of the present invention, the feature correction and alignment module includes:

[0019] The edge detection submodule adjusts the image contrast based on the iris image data, optimizes the clarity of the image edge by adjusting the brightness and contrast parameters, applies edge enhancement measures to locate the key edge of the iris, and generates an edge enhanced image;

[0020] The distortion correction submodule identifies and corrects optically induced distortion based on the edge enhanced image, and restores the original geometric shape of the image by redistributing and adjusting the pixel points in the image, including adjusting the position of each pixel and calibrating the perspective distortion, refining and adjusting the image, and obtaining a corrected image;

[0021] The pixel alignment submodule uses the corrected image to perform pixel-level alignment, adjusts according to the position difference of the iris features, adjusts the pixel position and matches the iris position during differential image acquisition, including fine-tuning the position of each pixel, and constructs a corrected iris image.

[0022] As a further solution of the present invention, the focus distance adjustment module includes:

[0023] The image analysis submodule identifies and analyzes key features in the iris based on the corrected iris image, including evaluating the feature distribution in the image, optimizing feature visibility through contrast and edge sharpening adjustments, and obtaining feature location data;

[0024] The focus adjustment submodule adjusts the lens focal length to match the user's real-time distance based on the feature positioning data, dynamically evaluates the distance between the iris feature and the lens, adjusts the focal length according to the evaluation result, optimizes the image clarity, and obtains a focus-optimized image;

[0025] The depth of field matching submodule uses the focus optimization image to adjust the focal length and aperture settings, including the aperture size required for depth of field requirement calculation, and adjusts the focal length to match the differentiated imaging requirements to construct an adjusted iris image.

[0026] As a further solution of the present invention, the calculation formula of the depth of field is:

[0027]

[0028] Among them, D f represents the required aperture diameter, f represents the focal length, N represents the aperture value, q represents the circle depth coefficient, m represents the imaging matching coefficient, and d o Represents the distance from the object to the lens.

[0029] As a further solution of the present invention, the user identity authentication module includes:

[0030] The feature analysis submodule scans the image based on the adjusted iris image, extracts the structural feature points of the iris by enhancing the contrast and details of key areas in the iris, adjusts the image processing parameters, optimizes the visibility and recognition accuracy of the features, and obtains feature mapping data;

[0031] The template comparison submodule uses the feature mapping data to compare the iris image features with the pre-stored iris templates in the database, analyzes the corresponding relationship between the image features and the templates, performs identity matching by calculating the feature similarity index, and obtains a verification similarity score;

[0032] The result evaluation submodule uses the verification similarity score to make identity authentication decisions, including aggregating and thresholding the scores to construct a preliminary identity authentication result.

[0033] As a further solution of the present invention, the identity matching algorithm module includes:

[0034] The iris feature extraction submodule analyzes the iris image based on the preliminary identity verification result, extracts key feature points in the image, including performing a resolution scan to determine a unique pattern of the iris, adjusting image processing parameters, and generating a feature detail mapping result;

[0035] The feature comparison submodule performs feature comparison based on the feature detail mapping result, and obtains the identity matching index by analyzing the correspondence between the iris feature and the template stored in the database, including detail comparison and difference analysis;

[0036] The verification result submodule uses the identity matching indicators and performs analysis and evaluation, including integrating comparison data, judging the consistency of the identity according to predetermined standards, verifying the authenticity of the identity by analyzing the accumulated matching data, and constructing the identity matching result.

[0037] As a further solution of the present invention, the security authentication output module includes:

[0038] The identity verification refinement submodule receives the task of iris recognition data based on the identity matching result, and compares it with the iris information pre-stored in the database, including pattern matching using the iris scan data, performing preliminary formatting processing, and obtaining identity verification data;

[0039] The payment initialization submodule starts the user's payment process based on the identity verification data, including verifying the user's payment eligibility from the security database, reviewing the account status, interacting with the financial service interface, initializing the payment command, and obtaining the payment transaction preparation status;

[0040] The security verification submodule utilizes the payment transaction preparation status and applies asymmetric encryption technology to verify the transaction preparation information, including checking whether the transaction conditions meet the security standards, evaluating the integrity of the payment request and the consistency of the user identity, and performing transaction security checks. By analyzing the data integrity and communication security during the transaction process, security authentication information and payment verification information are generated.

[0041] As a further solution of the present invention, the formula of the asymmetric encryption technology is as follows:

[0042]

[0043] Calculate the security score and generate security authentication information, where S represents the security score, H represents the hash value of the transaction information, P represents the user's public key data, D represents the integrity measurement of the transaction data, and a, b, and c are weight parameters.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, a multi-angle optical image sensor is configured and adjusted to cover differentiated acquisition distances, thereby improving the adaptability to users with different heights and enhancing the system's response speed to environmental changes. The application of edge detection technology optimizes image contrast, while correcting optical distortion and performing pixel-level image alignment, effectively combating image quality problems caused by depth of field changes. Not only is the image capture effect improved, but also the optimal image quality is ensured by finely adjusting the focus distance, thereby improving the accuracy and reliability of iris recognition. In secure mobile payment processing, the security of identity authentication is improved by detailed analysis of the iris image and comparison with the identity information in the database, reducing the risk of unauthorized access and fraud, and ensuring the legitimacy of the transaction and the financial security of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 This is a flow chart of the iris feature capture module in the present invention;

[0049] Figure 4 It is a flow chart of the feature correction and alignment module in the present invention;

[0050] Figure 5 is a flow chart of the focus distance adjustment module in the present invention;

[0051] Figure 6 It is a flow chart of the user identity authentication module in the present invention;

[0052] Figure 7 It is a flow chart of the identity matching algorithm module in the present invention;

[0053] Figure 8 This is a flow chart of the security authentication output module in the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 to Figure 2 , the secure mobile payment processing system based on iris recognition includes:

[0058] The iris feature capture module configures a multi-angle optical image sensor on the iris recognition hardware to capture iris images with differentiated depth of field, adjusts the optical image sensor coverage acquisition distance, matches differentiated user heights, controls image exposure under lighting conditions, and obtains iris image data;

[0059] The feature correction and alignment module uses edge detection to adjust image contrast, correct optical distortion, perform pixel-level image alignment, cope with differential depth of field changes, and generate a corrected iris image based on iris image data;

[0060] The focus distance adjustment module analyzes the corrected iris image, adjusts the focus according to the user distance, matches the iris features of the differentiated depth of field, optimizes the image capture effect, and obtains the adjusted iris image;

[0061] The user identity verification module uses the adjusted iris image to analyze the iris features, conducts basic identity verification that is not related to height, and compares the templates in the feature library to obtain a preliminary identity verification result;

[0062] The identity matching algorithm module performs feature analysis based on the preliminary identity authentication results, matches the iris patterns in the library, verifies identity consistency, and generates identity matching results;

[0063] The security authentication output module uses the identity matching results to perform identity authentication, receives and analyzes data from the iris recognition hardware, compares the identity information stored in the database, and after verifying the identity information, initiates secure mobile payment processing, including verifying the user account and initiating the payment transaction, and generating security authentication information and payment verification information.

[0064] Iris image data includes angle data and height matching indicators. The corrected iris image includes contrast enhancement results, distortion elimination effects, and alignment accuracy indicators. The adjusted iris image includes focal length adjustment value, depth of field adaptation, and image clarity. The preliminary identity authentication results include identity verification code, template matching degree, and verification success indicator. The identity matching results include feature similarity, pattern consistency index, and matching verification value. The security authentication information and payment verification information include verification status, account status verification, and transaction activation signal.

[0065] Specifically, if Figure 2 , 3 As shown, the iris feature capture module includes:

[0066] The optical sensing submodule configures a multi-angle optical image sensor on the iris recognition hardware, and uses angle adjustment and focal length optimization to capture iris raw image data. Through the adjustment of the angle of the optical image sensor and the control of the focus, the multi-level iris image is captured. The execution process of obtaining iris optical data is as follows;

[0067] A multi-angle optical image sensor is configured on the iris recognition hardware to capture the original image data of the iris through angle adjustment and focal length optimization, including subtle adjustment of the angle and precise control of the focus to adapt to the differentiated eye features and position changes of different users. The adjusted angle allows the sensor to capture the iris from multiple directions, and then ensure the clarity and accuracy of the image through focal length optimization. This method optimizes the image quality of the iris and improves the accuracy of recognition, thereby obtaining high-quality iris optical data.

[0068] The height matching submodule uses iris optical data to adjust the collection distance of the optical image sensor through the user's height information, takes continuous actions of measuring height and adjusting the position of the optical image sensor, optimizes the angle and position of image capture, and obtains the execution process of height optimized image data as follows;

[0069] By using the height information to adjust the collection distance of the optical image sensor, according to the formula:

[0070]

[0071] Calculate the optimal acquisition distance, where Q represents the acquisition distance, T represents the focal length, h represents the user's height, and J represents the standard height;

[0072] Set the standard height J to 170cm, the user height h to 160cm, and the focal length T to 50mm. According to the formula This is to adjust the collection distance according to the user's height and optimize the angle and position of iris image capture.

[0073] The exposure control submodule optimizes the image data based on the height, optimizes the image quality by adjusting the aperture size, exposure time and ISO setting, controls the exposure parameters of the optical image sensor, matches the differentiated lighting conditions, and obtains the iris image data. The execution process is as follows;

[0074] Image data is optimized based on height, and image quality is optimized by adjusting aperture size, exposure time and ISO settings. This involves fine control of the exposure parameters of the optical image sensor. Aperture adjustment can match differentiated lighting conditions, and exposure time adjustment ensures that the image will not be overexposed or underexposed. At the same time, ISO settings further improve image quality in low-light conditions. The adjustments work together to improve the quality of the final iris image data to obtain clear and highly recognizable iris image data.

[0075] Specifically, if Figure 2 , 4 As shown, the feature correction and alignment module includes:

[0076] The edge detection submodule adjusts the image contrast based on the iris image data, optimizes the clarity of the image edge by adjusting the brightness and contrast parameters, and applies edge enhancement measures to locate the key edges of the iris. The execution process of generating the edge enhanced image is as follows;

[0077] Adjust the image contrast and optimize the clarity of the image edge through the algorithm. According to the input iris image data, adjust the brightness and contrast parameters to improve the image quality. Enhance the local contrast of the image through dynamic range adjustment to make the key edges of the iris more prominent. Use edge detection algorithms, such as Canny or Sobel algorithms, to identify edge information in the image. The algorithm determines the edge position by calculating the gradient size of each pixel in the image. After a series of processing, the image is processed to obtain an edge-enhanced image that can clearly show the edge structure of the iris.

[0078] The distortion correction submodule identifies and corrects optically induced distortion based on edge-enhanced images, and restores the original geometric shape of the image by redistributing and adjusting the pixels in the image, including adjusting the position of each pixel and calibrating the perspective distortion, refining and adjusting the image, and obtaining the corrected image. The execution process is as follows;

[0079] Based on the edge-enhanced image, optically induced distortion is identified and corrected according to the formula:

[0080] f(x,y)=r·x′+l·y′+k

[0081] Calculate the corrected image, where f(x, y) represents the new coordinate position of each pixel point calculated by the correction function according to its original coordinates (x, y) during the distortion correction process, x and y represent the pixel coordinates in the original image, x′ and y′ represent the adjusted pixel coordinates, and r, l, k represent the correction parameters;

[0082] Referring to the position of the pixel coordinates that are key to optical distortion, the position of each pixel can be changed by adjusting the parameters r, l, and k to calibrate the perspective distortion and distortion. For example, if x=100, y=150, the adjustment parameters r=1.05, l=0.95, and k=2, then the corrected coordinates are x′=1.05·100+2=107, y′=0.95·150+2=144.5, so the corrected coordinates are (107, 144.5). This adjustment can restore the original geometric shape of the image and refine the adjusted image.

[0083] The pixel alignment submodule uses the corrected image to perform pixel-level alignment, adjusts the pixel position according to the position difference of the iris features, adjusts the pixel position and matches the iris position during the differential image acquisition, including fine-tuning the position of each pixel. The execution process of constructing the corrected iris image is as follows;

[0084] The corrected image is used for pixel-level alignment. According to the positional differences of the iris features, the pixel positions are adjusted. By comparing the distortion-corrected iris image with the original image, the minimum positional deviation is found. Then, image processing techniques such as affine transformation are used to accurately adjust the position of each pixel to match the iris features by calculating the number of pixels that need to be moved. This includes not only linear movement but also rotation or more complex transformations to ensure maximum pixel alignment accuracy. Through calculation and adjustment, the corrected iris image is constructed.

[0085] Specifically, if Figure 2 , 5 As shown, the focus distance adjustment module includes:

[0086] The image analysis submodule identifies and analyzes the key features in the iris based on the corrected iris image, including evaluating the feature distribution in the image, and optimizing the feature visibility through contrast and edge sharpening adjustments. The execution process of obtaining feature location data is as follows;

[0087] Based on the corrected iris image, the image is first preprocessed, including noise removal and color balance adjustment to ensure that the image quality meets the analysis requirements. Image processing technology is used to analyze the key features in the iris, such as texture, color and morphology. The features are identified by advanced image recognition techniques such as edge detection and texture analysis algorithms. For edge detection, the Sobel operator is used to identify the precise boundaries of the iris features. The Gabor filter is also used to process the image to extract more detailed texture information. In order to enhance the visibility of features in the image, the image is contrast adjusted and sharpened. Contrast adjustment is achieved through histogram equalization. The technology can improve the global contrast of the image and make the features more obvious. Sharpening uses Laplace filters to enhance the edge information of the image, making the details of the iris clearer. The system accurately obtains feature location data through the enhanced and adjusted images. The data will be used for subsequent focus adjustment and image analysis to ensure the efficiency and accuracy of the system, including evaluating the feature distribution in the image, and optimizing feature visibility through contrast and edge sharpening adjustments to obtain feature location data.

[0088] The focus adjustment submodule adjusts the lens focal length to match the user's real-time distance based on the feature positioning data, dynamically evaluates the distance between the iris feature and the lens, adjusts the focal length according to the evaluation results, optimizes the image clarity, and obtains the execution process of the focus optimized image as follows;

[0089] Based on the feature positioning data, the real-time distance between the user and the camera lens is first calculated by an algorithm. The calculation is based on the iris feature position and the pre-set benchmark data. By comparing the data, the system can dynamically evaluate the distance between the iris feature and the lens. After the distance data is obtained, the electric drive mechanism is used to automatically adjust the focal length of the lens to match the real-time position of the user. This adjustment is continuous and dynamic, ensuring that the best image clarity can be obtained at all times. The submodule will also adjust the exposure and aperture settings based on the real-time image quality feedback to further optimize the image clarity. This optimization is completed through real-time image analysis. During the analysis process, the current image clarity will be evaluated and compared with the target clarity standard to determine whether further adjustment is needed. Through fine adjustment, it can ensure that the final focus-optimized image is optimal in both clarity and detail display. The distance between the iris feature and the lens is dynamically evaluated, and the focal length is adjusted according to the evaluation results to optimize the image clarity and obtain the focus-optimized image.

[0090] The depth of field matching submodule uses the focus optimization image to adjust the focal length and aperture settings, including the aperture size required for the depth of field requirement calculation, and adjusts the focal length to match the differentiated imaging requirements. The execution process of constructing the adjusted iris image is as follows;

[0091] The formula for calculating depth of field is:

[0092]

[0093] Among them, D f represents the required aperture diameter, f represents the focal length, N represents the aperture value, q represents the circle depth coefficient, m represents the imaging matching coefficient, and d o Represents the distance from the object to the lens;

[0094] This formula comprehensively considers the factors of focal length, aperture size, and imaging distance. By introducing the circle depth coefficient and imaging matching coefficient, it can more accurately adjust the focal length and aperture to meet differentiated imaging needs and improve image quality.

[0095] The formula is used to calculate the reference object distance d at a specific focal length f and aperture value N. o In this case, the aperture size D needs to be set f To obtain clear iris images, the quantification and numerical setting of parameters are based on actual measurement and standardization methods;

[0096] Focal length f: In this example, a medium focal length such as 50mm, commonly used in portrait photography, is selected. This value is directly read from the physical properties of the camera lens.

Claims

1. A secure mobile payment processing system based on iris recognition, characterized in that: The system comprises: The iris feature capture module configures a multi-angle optical image sensor on the iris recognition hardware to capture iris images with differentiated depth of field, adjusts the sensor to match the user's height, controls image exposure, and obtains iris image data; The feature correction and alignment module uses edge detection to adjust image contrast, correct optical distortion, perform pixel-level image alignment, cope with differential depth of field changes, and generate a corrected iris image based on the iris image data; The focus distance adjustment module analyzes the corrected iris image, matches the iris features of the differentiated depth of field, optimizes the image capture effect, and obtains the adjusted iris image; The user identity verification module uses the adjusted iris image to analyze the iris features, perform basic identity verification that is not related to height, and compare the templates in the feature library to obtain a preliminary identity verification result; The identity matching algorithm module performs feature analysis based on the preliminary identity authentication result, matches the iris pattern in the library, verifies identity consistency, and generates an identity matching result; The security authentication output module uses the identity matching result to perform identity authentication, analyze the iris recognition hardware data, compare the identity information in the database, start the secure mobile payment processing, and generate security authentication information and payment verification information.

2. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The iris image data includes angle data and height matching index; the corrected iris image includes contrast enhancement results, distortion elimination effects, and alignment accuracy index; the adjusted iris image includes focal length adjustment value, depth of field adaptation, and image clarity; the preliminary identity authentication result includes identity verification code, template matching degree, and verification success index; the identity matching result includes feature similarity, pattern consistency index, and matching verification value; the security authentication information and payment verification information include verification status, account status verification, and transaction activation signal.

3. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The iris feature capture module comprises: The optical sensing submodule configures a multi-angle optical image sensor on the iris recognition hardware, uses angle adjustment and focal length optimization to capture iris original image data, and captures multi-level iris images and obtains iris optical data through the adjustment of the angle of the optical image sensor and the control of the focus. The height matching submodule uses the iris optical data to adjust the collection distance of the optical image sensor according to the user's height information, takes continuous actions of measuring the height and adjusting the position of the optical image sensor, optimizes the angle and position of image capture, and obtains height-optimized image data; The exposure control submodule optimizes the image data based on the height, optimizes the image quality by adjusting the aperture size, exposure time and ISO setting, controls the exposure parameters of the optical image sensor, matches the differentiated lighting conditions, and obtains the iris image data.

4. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The feature correction and alignment module comprises: The edge detection submodule adjusts the image contrast based on the iris image data, optimizes the clarity of the image edge by adjusting the brightness and contrast parameters, applies edge enhancement measures to locate the key edge of the iris, and generates an edge enhanced image; The distortion correction submodule identifies and corrects optically induced distortion based on the edge enhanced image, and restores the original geometric shape of the image by redistributing and adjusting the pixel points in the image, including adjusting the position of each pixel and calibrating the perspective distortion, refining and adjusting the image, and obtaining a corrected image; The pixel alignment submodule uses the corrected image to perform pixel-level alignment, adjusts according to the position difference of the iris features, adjusts the pixel position and matches the iris position during differential image acquisition, including fine-tuning the position of each pixel, and constructs a corrected iris image.

5. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The focus distance adjustment module comprises: The image analysis submodule identifies and analyzes key features in the iris based on the corrected iris image, including evaluating the feature distribution in the image, optimizing feature visibility through contrast and edge sharpening adjustments, and obtaining feature location data; The focus adjustment submodule adjusts the lens focal length to match the user's real-time distance based on the feature positioning data, dynamically evaluates the distance between the iris feature and the lens, adjusts the focal length according to the evaluation result, optimizes the image clarity, and obtains a focus-optimized image; The depth of field matching submodule uses the focus optimization image to adjust the focal length and aperture settings, including the aperture size required for depth of field requirement calculation, and adjusts the focal length to match the differentiated imaging requirements to construct an adjusted iris image.

6. The secure mobile payment processing system based on iris recognition according to claim 5, characterized in that: The calculation formula of the depth of field is: Among them, D f represents the required aperture diameter, f represents the focal length, N represents the aperture value, q represents the circle depth coefficient, m represents the imaging matching coefficient, and d o Represents the distance from the object to the lens.

7. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The user identity authentication module comprises: The feature analysis submodule scans the image based on the adjusted iris image, extracts the structural feature points of the iris by enhancing the contrast and details of key areas in the iris, adjusts the image processing parameters, optimizes the visibility and recognition accuracy of the features, and obtains feature mapping data; The template comparison submodule uses the feature mapping data to compare the iris image features with the pre-stored iris templates in the database, analyzes the corresponding relationship between the image features and the templates, performs identity matching by calculating the feature similarity index, and obtains a verification similarity score; The result evaluation submodule uses the verification similarity score to make identity authentication decisions, including aggregating and thresholding the scores to construct a preliminary identity authentication result.

8. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The identity matching algorithm module includes: The iris feature extraction submodule analyzes the iris image based on the preliminary identity verification result, extracts key feature points in the image, including performing a resolution scan to determine a unique pattern of the iris, adjusting image processing parameters, and generating a feature detail mapping result; The feature comparison submodule performs feature comparison based on the feature detail mapping result, and obtains the identity matching index by analyzing the correspondence between the iris feature and the template stored in the database, including detail comparison and difference analysis; The verification result submodule uses the identity matching indicators and performs analysis and evaluation, including integrating comparison data, judging the consistency of the identity according to predetermined standards, verifying the authenticity of the identity by analyzing the accumulated matching data, and constructing the identity matching result.

9. The secure mobile payment processing system based on iris recognition according to claim 1, characterized in that: The safety authentication output module comprises: The identity verification refinement submodule receives the task of iris recognition data based on the identity matching result, and compares it with the iris information pre-stored in the database, including pattern matching using the iris scan data, performing preliminary formatting processing, and obtaining identity verification data; The payment initialization submodule starts the user's payment process based on the identity verification data, including verifying the user's payment eligibility from the security database, reviewing the account status, interacting with the financial service interface, initializing the payment command, and obtaining the payment transaction preparation status; The security verification submodule utilizes the payment transaction preparation status and applies asymmetric encryption technology to verify the transaction preparation information, including checking whether the transaction conditions meet the security standards, evaluating the integrity of the payment request and the consistency of the user identity, and performing transaction security checks. By analyzing the data integrity and communication security during the transaction process, security authentication information and payment verification information are generated.

10. The secure mobile payment processing system based on iris recognition according to claim 9, characterized in that: The formula of the asymmetric encryption technology is as follows: Calculate the security score and generate security authentication information, where S represents the security score, H represents the hash value of the transaction information, P represents the user's public key data, D represents the integrity measurement of the transaction data, and a, b, and c are weight parameters.

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