Palm vein high-security identity authentication system based on spectral analysis and deep learning

Abstract

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

Description

Technical Field

[0001] This invention relates to the field of biometrics, specifically to a high-security palm vein authentication system based on spectral analysis and deep learning. Background Technology

[0002] According to Chinese Patent Application Publication No. CN119131850A, a palm vein-based identity authentication method and system are disclosed. The palm vein-based identity authentication system includes: an identity information acquisition module, a palm vein image acquisition module, an environmental data acquisition module, a feature extraction module, a matching value calculation module, an authentication threshold adjustment module, and an identity authentication module. This invention performs multi-angle fusion during the palm vein feature extraction process, ensuring that the image used for feature extraction closely matches the palm vein image during registration. This avoids discrepancies caused by user actions during identity authentication, reducing the false alarm rate. When calculating the matching value, it not only calculates the direct distance between the user's palm vein feature vector and the authenticated palm vein feature vector corresponding to the user's identity information in the identity database, but also considers the cumulative distance caused by changes in user actions, thereby mitigating the impact of changes in actions and reducing the false alarm rate in actual use.

[0003] The aforementioned patent documents and prior art have the following technical problems when used: Problem 1: Traditional palm vein recognition systems rely on single-distribution inference or static texture analysis, making them susceptible to being fooled by highly realistic biological materials (such as silicone models or 3D-printed replicas), resulting in insufficient recognition security and defense capabilities. Problem 2: Existing palm vein recognition methods use limited data and cannot capture the dynamic physiological characteristics of palm veins. Although deep learning has been introduced into the field of biometrics, it is usually only used for processing single-modal data and fails to fully utilize multi-dimensional information in the spectral and temporal domains, resulting in inaccurate recognition. Thirdly, traditional handheld recognition systems lack a dynamic adjustment mechanism, making it difficult to adapt to individual differences among users (such as the thickness of veins on the palm and skin color) and environmental changes (such as skin color and temperature), and lacking the ability to adapt to individual differences and environmental changes. Summary of the Invention

[0004] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a high-security palm vein authentication system based on spectral analysis and deep learning, solving the following problems: 1. Addressing the issue of insufficient defensive capabilities in complex biomimetic techniques; 2. Addressing the issue of limited data in palm vein recognition, leading to inaccurate recognition and low efficiency; 3. Addressing the issue of a lack of adaptability to individual differences and environmental changes.

[0005] Technical solution To achieve the above objectives, the present invention provides the following technical solution: a high-security palm vein authentication system based on spectral analysis and deep learning, wherein the system comprises a hardware layer, a data processing layer, an algorithm layer, and an application layer, wherein: The hardware layer includes a hyperspectral imaging module, which uses a quantum dot-enhanced hyperspectral camera and a multi-band tunable light source, covering the 200-2500nm band, for acquiring and managing hyperspectral dynamic data. The data processing layer includes a data preprocessing module and a data compression and fragmentation module, which are used to organize, enhance and multi-dimensionally model hyperspectral data, and reconstruct compressed data through self-organizing features. The algorithm layer includes a spectral spatiotemporal feature extraction module, an adversarial liveness detection module, an identity authentication module, and a privacy protection module. The spectral spatiotemporal feature extraction module uses a spectral spatiotemporal graph network (ST-GNN) to extract palm vein features. The adversarial liveness detection module combines physical modeling and generative adversarial networks to achieve liveness detection. The privacy protection module generates anonymous authentication results through zero-knowledge proof. The application layer includes a user interaction module and an output and integration module, which are used to provide user interface interaction and output encrypted authentication results; The system acquires data and performs real-time identity authentication through hyperspectral dynamic reasoning, spectral spatiotemporal graph neural networks, and quantum entanglement heuristic optimization algorithms, and enhances security and privacy through adversarial liveness detection and cross-modal privacy protection.

[0006] Preferably, the hyperspectral imaging module of the hardware layer utilizes quantum dot photosensitive materials to enhance the ability to capture weak signals, achieving a resolution of more than 100 spectral channels, and supports pulsed illumination of ultraviolet, visible, near-infrared, and mid-infrared light. This is used to capture the dynamic interaction and absorption characteristics of pins in different topologies. The hyperspectral imaging module includes a high-throughput beam splitter and a microbeam array to ensure spectral resolution and imaging quality. The hyperspectral imaging module generates palm vein data containing spatial, spectral, and temporal dimensions through dynamic pulsed illumination and quantum dot enhancement technology.

[0007] Preferably, the hardware layer further includes an auxiliary acquisition unit and a hardware acceleration unit. The auxiliary acquisition unit includes an environmental sensor and a motion compensation device. The environmental sensor detects temperature, humidity, and prompt intensity to enhance adjustment parameters. The motion compensation device corrects hand sleep based on a gyroscope and an infrared rangefinder. The hardware acceleration unit includes an FPGA chip and an edge computing module. The FPGA chip is used for real-time monitoring and channel separation of hyperspectral data. The edge computing module implements functional features to reduce data transmission burden.

[0008] Preferably, the data preprocessing module of the data processing layer generates background noise based on the spectral reflectance defined by Lambert-Beer, enhances the weak spectral signal caused by blood flow through time series analysis, and transforms the hyperspectral tissue data into a three-dimensional tensor containing spatial, spectral, and temporal dimensions, providing multidimensional input for subsequent algorithms.

[0009] Preferably, the data compression and sharding module of the data processing layer performs self-organizing feature increments without panic supervision, extracts key feature subsets from hyperspectral data, compresses data dimensions, shards the compressed data, and encrypts and transmits it to the algorithm layer, ensuring the security of data transmission.

[0010] Preferably, the spectral spatiotemporal feature extraction module of the algorithm layer adopts a spectral spatiotemporal graph neural network (ST-GNN), which takes the hyperspectral palm vein three-dimensional tensor as input to construct a dynamic graph structure, where nodes represent features of different spectral bands and edges represent cross-spectral and time series correlations. The spatial features of each wavelength are extracted through the spectral graph topology layer, the spatiotemporal attention layer captures the dynamic changes in the time series and assigns cross-spectral weights, the feature fusion layer generates a global palm vein feature map through graph pooling, and the network hyperparameters are dynamically adjusted using a quantum entanglement-inspired optimization algorithm, achieving fast convergence and high robustness of features.

[0011] Preferably, the adversarial liveness detection module of the algorithm layer analyzes the absorption characteristics of palm veins in the hyperspectral range through physical simulation, calculates the difference between oxyhemoglobin and deoxyhemoglobin, detects periodic spectral perturbations caused by heartbeats, generates highly realistic fake samples using an adversarial generative network, trains a discriminator to distinguish between real palm veins and fake samples, outputs liveness authentication confidence, and combines the physical modeling results with the adversarial learning results to ensure high accuracy and anti-formatting capability of liveness detection.

[0012] Preferably, the identity authentication module of the algorithm layer calculates the similarity of features through cosine distance or Hamming distance, and dynamically updates the template library through online learning. The privacy protection module generates anonymous authentication templates through unpredictable transformations, and realizes identity verification without exposing original data based on the zk-SNARK zero-knowledge proof protocol. It also performs cross-modal fusion by combining voice or behavioral features to further enhance authentication security.

[0013] Preferably, the user interaction module of the application layer provides an interface through a touch screen or contactless breathing operation and displays the authentication status in real time. The output and integration module generates an encrypted authentication token that is compatible with the OAuth protocol and supports seamless integration with external systems. The application layer further includes an adaptive management unit that adjusts strategies and algorithm parameters based on environmental data. Sensors record and anonymize authentication logs for auditing and optimization.

[0014] Beneficial effects This invention provides a high-security palm vein authentication system based on spectral analysis and deep learning. It offers the following advantages: 1. This invention employs an adversarial liveness detection module within the algorithm layer, combined with physical modeling and generative adversarial networks (GANs), to effectively identify complex imitations (such as highly biomimetic materials). Physical modeling analyzes the absorption characteristics of palm veins in the hyperspectral range (such as the difference between oxyhemoglobin and oxyhemoglobin) and periodic spectral micro-interferences caused by heart rate, providing liveness evidence based on physiological dynamics. Simultaneously, GAN generates highly realistic template samples to train the discriminator, further enhancing the system's ability to detect template methods. Compared to single traditional texture or static signal detection, this system combines physical characteristics with deep learning, achieving a liveness detection confidence level of up to 99.99%.

[0015] 2. This invention employs a hardware-level hyperspectral imaging module (covering 200-2500 pixels). Hyperspectral imaging, through quantum dot enhancement technology and dynamic pulsed illumination, generates palm vein data containing spatial, spectral, and temporal dimensions, comprehensively capturing dynamic physiological characteristics such as blood flow and oxygenation status. ST-GNN, through spectral image shape and spatiotemporal attention, mines fine features across spectral and temporal series, generating global mechanism vein feature mining. The multidimensional data processing capability not only improves feature discrimination but also enables the system to accurately characterize the dynamic changes of palm veins, overcoming the shortcomings of existing technologies in capturing dynamic physiological features.

[0016] 3. This invention employs a system hardware layer equipped with an auxiliary acquisition unit (sensor environment and motion compensation device), a data processing layer that introduces self-organizing features, and an application layer with an adaptive management unit. Together, these measures address the problem of existing technologies lacking the ability to adapt to individual differences and environmental changes. The environmental sensor monitors parameters such as temperature, humidity, and signal strength in real time, dynamically adjusting adaptive and algorithm settings. Organizational features are extracted from hyperspectral data to adapt to the different habits and vein differences of different users. The adaptive management optimizes model parameters based on module usage feedback, ensuring robustness in various configuration scenarios. Furthermore, the quantum entanglement optimization algorithm further accelerates feature regression, reducing the false recognition rate (FAR) of the system to below 0.001% under individual and environmental changes, significantly improving practicality and reliability. Attached Figure Description

[0017] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a diagram illustrating the system operation steps of the present invention; Figure 3 This is a schematic diagram illustrating the ability of the present invention to capture dynamic physiological features using hyperspectral imaging. Figure 4 Line graph showing the absorption difference versus confidence level between the physical modeling and GAN-based liveness detection of this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1: like Figures 1 to 4 As shown, a high-security palm vein authentication system based on spectral analysis and deep learning is presented. The system comprises a hardware layer, a data processing layer, an algorithm layer, and an application layer, wherein: The hardware layer includes a hyperspectral imaging module, employing a quantum dot-enhanced hyperspectral camera and a multi-band tunable light source covering the 200-2500nm wavelength range. This module is used to acquire dynamic hyperspectral data of the palm veins. It utilizes quantum dot photosensitive materials to enhance the capture of weak signals, achieving a resolution of over 100 spectral channels and supporting pulsed illumination of ultraviolet, visible, near-infrared, and mid-infrared light. This allows for the capture of the dynamic interaction and absorption characteristics of the veins in different topologies. The hyperspectral imaging module includes a high-throughput beam splitter and a microbeam array to ensure spectral resolution and imaging quality. It generates palm vein data containing spatial, spectral, and temporal dimensions through dynamic pulsed illumination and quantum dot enhancement technology. The hardware layer also includes an auxiliary acquisition unit and a hardware acceleration unit. The auxiliary acquisition unit includes an environmental sensor and a motion compensation device. The environmental sensor detects temperature, humidity, and cue intensity. Enhanced adjustment parameters and a motion compensation device based on a gyroscope and infrared rangefinder correct hand sleep patterns. The hardware acceleration unit includes an FPGA chip and an edge computing module. The FPGA chip is used for real-time monitoring and channel separation of hyperspectral data, while the edge computing module implements functional features to reduce data transmission burden. The hardware-layer hyperspectral imaging module (covering 200-2500) generates palm vein data containing spatial, spectral, and temporal dimensions through quantum dot enhancement technology and dynamic pulse illumination, fully capturing dynamic physiological characteristics such as blood flow and oxygenation status. ST-GNN, through spectral shape and spatiotemporal attention, mines fine features across spectral and temporal series, generating global mechanism vein feature mining. The multidimensional data processing capability not only improves feature discrimination but also enables the system to accurately characterize the dynamic changes of palm veins, overcoming the shortcomings of existing technologies in capturing dynamic physiological features.

[0020] The data processing layer includes a data preprocessing module and a data compression and fragmentation module. These modules are used to organize, enhance, and multi-dimensionally model hyperspectral data. The compressed data is reconstructed using self-organizing features. The data preprocessing module generates background noise based on the Lambert-Beer spectral reflectance standard and enhances weak spectral signals caused by blood flow through time-series analysis. This transforms the hyperspectral tissue data into a three-dimensional tensor containing spatial, spectral, and temporal dimensions, providing multi-dimensional input for subsequent algorithms. The data compression and fragmentation module performs panic-free supervised execution of self-organizing feature increments, extracting key feature subsets from the hyperspectral data, compressing the data dimensions, fragmenting the compressed data, and encrypting it before transmitting it to the algorithm layer, ensuring data transmission security. The algorithm layer includes a spectral spatiotemporal feature extraction module, an adversarial liveness detection module, an authentication module, and a privacy protection module. The spectral spatiotemporal feature extraction module uses a spectral spatiotemporal graph neural network (ST-GNN) to extract palm vein features. The adversarial liveness detection module combines physical modeling and generative adversarial networks to achieve liveness detection. The privacy protection module generates anonymous authentication results through zero-knowledge proofs. The spectral spatiotemporal feature extraction module of the algorithm layer uses a spectral spatiotemporal graph neural network (ST-GNN), taking the hyperspectral palm vein 3D tensor as input to construct a dynamic graph structure, where nodes represent features of different spectral bands, and edges represent cross-spectral features. The correlation of time series data is analyzed by extracting spatial features of each wavelength through a spectral topology layer. A spatiotemporal attention layer captures dynamic changes in the time series and assigns cross-spectral weights. A feature fusion layer generates a global palm vein feature map through graph pooling. A quantum entanglement-inspired optimization algorithm is used to dynamically adjust the network hyperparameters, achieving fast convergence and high robustness. The adversarial liveness detection module in the algorithm layer analyzes the absorption characteristics of palm veins in the hyperspectral range through physical simulation, calculates the difference between oxyhemoglobin and deoxyhemoglobin, and detects periodic spectral perturbations caused by heartbeats. A highly realistic fake sample is generated using an adversarial generative network. The trained discriminator distinguishes between real palm veins and forged samples, outputting a liveness detection confidence score. It combines physical modeling results with adversarial learning results to ensure high accuracy and anti-formatting capabilities in liveness detection. The algorithm-layer's authentication module calculates feature similarity using cosine or Hamming distance and dynamically updates the template library through online learning. The privacy protection module generates anonymous authentication templates through unpredictable substitution and achieves authentication without exposing raw data based on the zk-SNARK zero-knowledge proof protocol. Cross-modal fusion combining voice or behavioral features further enhances authentication security. Adversarial liveness detection in the algorithm layer is then implemented. This module, combining physical modeling and generative adversarial networks (GANs), enables effective identification of complex imitations (such as highly biomimetic materials). Physical modeling analyzes the absorption characteristics of palm veins in the hyperspectral range (such as the difference between oxyhemoglobin and oxyhemoglobin) and the periodic spectral micro-interferences caused by heart rate, providing bio-evidence based on physiological dynamics. Simultaneously, GAN generates highly realistic template samples to train the discriminator, further enhancing the system's ability to understand template methods. Compared with single traditional texture or static signal detection, this system combines physical characteristics with deep learning, achieving a bio-detection confidence level of up to 99.99%.

[0021] The application layer includes a user interaction module and an output and integration module. It provides a user interface and outputs encrypted authentication results. The user interaction module provides an interface via a touchscreen or contactless breathing operation and displays the authentication status in real time. The output and integration module generates encrypted authentication tokens, is compatible with the OAuth protocol, and supports seamless integration with external systems. The application layer further includes an adaptive management unit that adjusts strategies and algorithm parameters based on environmental data. Sensors record and anonymize authentication logs for auditing and optimization. The system achieves data acquisition and real-time identity authentication through hyperspectral dynamic inference, spectral spatiotemporal graph neural networks, and quantum entanglement heuristic optimization algorithms. Security and privacy are enhanced through adversarial liveness detection and cross-modal privacy protection. By employing an auxiliary acquisition unit (sensor environment and motion compensation device) at the system hardware layer, introducing self-organizing features at the data processing layer, and setting an adaptive management unit at the application layer, this approach addresses the lack of adaptability to individual differences and environmental changes in existing technologies. Environmental sensors monitor parameters such as temperature, humidity, and signal strength in real time, dynamically adjusting adaptive and algorithm settings. Organizational features are extracted from hyperspectral data to adapt to the different vein patterns of various users. Adaptive management optimizes model parameters based on module usage feedback, ensuring robustness in various configuration scenarios. Furthermore, a quantum entanglement-based optimization algorithm further accelerates feature regression, reducing the false recognition rate (FAR) of the system to below 0.001% under individual and environmental changes, significantly improving practicality and reliability. Specific Implementation Example 2: like Figures 1 to 4 As shown, based on the content of the above specific embodiments, the following content is further disclosed: The entire system operates as follows: SP1: User initiates authentication and triggers hardware layer data acquisition: The user sets the scanning area of ​​the hyperspectral imaging module and initiates an authentication request through the user interaction module (touchscreen or contactless communication). After the system detects the user's operation, the hardware layer enters the working state, the user interaction module displays the "scanning" prompt, and the auxiliary acquisition unit (environmental sensor) of the hardware layer simultaneously detects environmental parameters (such as temperature, humidity, antenna strength) to adjust the strategy settings. Sp2: Hyperspectral Imaging Module Acquires Dynamic Data: The hardware-level hyperspectral imaging module utilizes a quantum dot-enhanced hyperspectral camera and a multi-band tunable light source (covering 200-2500nm) to acquire hyperspectral dynamic data from the pipeline through dynamic pulse illumination. The multi-band light source sequentially emits visible light, near-infrared, and mid-infrared pulses to capture the reflection and absorption characteristics of the pins in different bands. Quantum dot sensing materials enhance weak signals, achieving a resolution of 100+ spectral channels. High-throughput beam splitters and microbeams ensure spectral resolution and imaging quality. Motion compensation devices (gyroscope and infrared rangefinder) correct hand movements to ensure data stability. SP3: The hardware acceleration unit performs real-time repair of the raw data: The FPGA chip of the hardware acceleration unit performs real-time acquisition and channel separation of the hyperspectral raw data. The edge computing module executes the data to be acquired, which increases the burden of subsequent data processing. The FPGA chip generates background noise and separates each spectral channel. The edge computing module extracts spatial features and generates the initial data packet. SP4: The data processing layer adjusts and enhances the data: The data composition module of the data processing layer receives the data transmitted from the hardware layer, sets the spectral reflectance based on the Lambert-Beer algorithm, and enhances the dynamic signal through time series analysis. The formulation process aims to improve the performance of the data while preserving the spectral characteristics of the data. Time series analysis enhances the weak spectral perturbations caused by blood flow (such as heartbeat signals). The output is organized into a three-dimensional tensor (spatial dimension, dimensional dimension, and time dimension). SP5: Data Compression and Fragmented Encrypted Transmission: The data compression and fragmentation module of the data processing layer compresses data dimensions through self-organizing features. The text-compressed data is fragmented and encrypted before being transmitted to the algorithm layer. No key supervisory feature subset is established, reducing production information. Fragmented data is protected by encryption algorithms (such as AES) to ensure transmission security. Sp6: The Spectral-Spatiotemporal Extraction Module Generates and Collects Vein Feature Analysis: The Spectral-Spatiotemporal Graph Neural Network (ST-GNN) of the algorithm layer receives three-dimensional tensor data, extracts multi-dimensional palm vein features, and generates global feature management. It models the data as a dynamic graph structure, with nodes representing spectral fluctuation features and edges representing cross-spectral and temporal correlations. The spectral graph structure layer extracts spatial features, the spatiotemporal attention layer assigns dynamic weights, and the feature fusion layer generates feature distributions (dimensions approximately 512-1024) through graph pooling. A quantum entanglement-inspired optimization algorithm dynamically adjusts hyperparameters (such as the learning rate) to ensure fast convergence and high robustness. Sp7: Adversarial liveness detection module verifies liveness status: The adversarial liveness detection module in the algorithm layer combines physical modeling and generative networks to determine whether the input data comes from a live person. Physical modeling analyzes absorption characteristics (difference between oxyhemoglobin and deoxyhemoglobin) and periodic perturbations caused by heartbeat. The generator of GAN generates original samples, the discriminator is trained to distinguish between real and original data, and outputs the liveness confidence score. If it is lower than the threshold (e.g., 99%), the authentication process is terminated and a "non-liveness" warning is returned. SP8: The identity authentication module performs feature matching: The identity authentication module of the algorithm layer receives the feature code, performs approximate calculation with the template library, confirms the user's identity, calculates feature similarity using cosine distance or Hamming distance, and the online learning mechanism dynamically updates the template library according to the matching results to adapt to long-term feature changes. If the approximate value exceeds the preset threshold (such as 95%), the identity authentication is confirmed to be successful. Sp9: Privacy protection module generates anonymous authentication results: The privacy protection module of the algorithm layer anonymizes the authentication results and generates authentication credentials for obtaining the original data through zero-knowledge proof. Irreversible transformations (such as hash functions) generate anonymous templates, and the zk-SNARK protocol implements zero-knowledge verification to ensure privacy and security. It can optionally combine voice or behavioral features for cross-modal fusion to enhance security. SP10: Application Layer Output Authentication Results and Adaptive Optimization: The application layer user interaction module displays the authentication status, the output and integration module generates encrypted authentication tokens, the adaptive management module optimizes system parameters, the user interaction module displays "authentication successful" or "authentication failed", and the output and integration module generates encrypted tokens compatible with the OAuth protocol for use by external systems via touch screen or contactless operation keyboard. The adaptive management unit adjusts parameters and algorithm models based on environmental sensor data and authentication logs, and records anonymized logs for auditing.

[0023] Hardware layer (Sp1-Sp3): Acquires high-precision hyperspectral dynamic data and performs preliminary processing to ensure the accuracy and error of the input data; Data processing layer (Sp4-Sp5): Adjusts and enhances data and compresses and encrypts it, providing efficient and secure multidimensional input for the algorithm layer; Algorithm layer (Sp6-Sp9): High-security authentication is achieved by extracting adversarial features through ST-GNN, verifying liveness detection, confirming identity through feature matching, and generating anonymous results for privacy protection; Application layer (Sp10): Outputs authentication results and dynamically optimizes the system, user experience, and long-term stability. Specific Implementation Example 3: like Figures 1 to 4 As shown, based on the content of the above specific embodiments, the following content is further disclosed: The algorithm of the entire system is as follows: Hyperspectral imaging module: Acquires hyperspectral dynamic data of palm veins.

[0025] in: In spatial coordinates Hyperspectral intensity at wavelength and time t; Palmar vein wavelength Reflectivity; The illumination intensity of a multi-band light source at wavelength λ and time t; Spatial texture characteristics of palm veins; Noise items (such as ambient light or sensor noise).

[0026] Multi-band light sources emit pulsed light sequentially (ultraviolet, visible, near-infrared, and mid-infrared). A quantum dot-enhanced camera captures the reflected signals, generating multi-channel data. A motion compensation device corrects jitter and ensures spatial consistency. Dynamic pulsed illumination captures temporal changes, and quantum dot enhancement improves the resolution of weak signals, generating three-dimensional data containing spatial, spectral, and temporal dimensions, providing high-quality input for subsequent multi-dimensional feature extraction.

[0027] In the FPGA construction of the hardware acceleration unit, noise reduction filtering is used:

[0028] in: The intensity data is after denoising; For the domain window; The number of pixels within the window; For noise estimation within the domain; The FPGA removes high-frequency noise in real time through mean filtering, and the edge computing module initially extracts spatial features, reducing the data transmission burden and improving the efficiency of subsequent processing.

[0029] Data preprocessing module: calibrates spectral reflectance and enhances dynamic signals, using Lambert-Beer law calibration.

[0030] in: wavelength Absorption at that location; : The intensity of the collected light; Reference light intensity (background light without palmar veins); : Molar absorptivity at wavelength λ; The concentration of absorbable substances (such as hemoglobin) in the blood; Optical path length.

[0031] Calculate absorbance Remove background noise.

[0032] Time series analysis: Perform a Fourier transform to extract periodic signals (such as heartbeats):

[0033] in, For frequency Spectral intensity at that location; Blood flow signals are enhanced through absorbance calibration, and three-dimensional tensors are generated. It overcomes the problem of single data dimension and provides multidimensional input of dynamic physiological characteristics. Data compression and fragmentation module:

[0034] in: : Compressed feature subset; : No. One hyperspectral data sample; : No. The nth sample pair The weights of each feature; Number of feature subsets; Regularization parameters; Extracting K-means clustering One key feature is that L1 regularization reduces redundant dimensions. Fragmentation and encrypted transmission are then performed. It compresses data and ensures secure transmission, improving both computing efficiency and security.

[0035] Update of the graph neural network in the spectral spatiotemporal feature extraction module:

[0036] in: : No. Layer node feature matrix (initially a three-dimensional tensor) T (spectral characteristics of T) Normalized adjacency matrix This is the spectral and temporal correlation matrix. For degree matrix, It is the identity matrix; : No. Layer weight matrix; : Bias term; Activation function, such as ReLU.

[0037] Spatiotemporal attention mechanism:

[0038] in: node and Attention weights between; This is the attention weight matrix; This involves concatenating vectors. Quantum optimization:

[0039] in: The learning rate; The gradient of the loss function; This is a heuristic perturbation for quantum entanglement.

[0040] Nodes represent spectral features, edges represent cross-spectral and temporal correlations, graph convolution extracts spatial features, attention layers assign spatiotemporal weights, graph pooling generates a global feature vector F, mining multidimensional features, improving discriminative power, overcoming the inadequacy of capturing dynamic characteristics, and exhibiting high robustness in feature extraction (FAR < 0.001%).

[0041] Adversarial liveness detection module:

[0042] in: Poor absorption of oxygenated and deoxygenated hemoglobin; , The absorbance coefficients of oxygenated and deoxygenated hemoglobin; , For concentration; Optical path length; The GAN loss function is:

[0043] in, For the discriminator output, Forged samples generated by the generator , For real and noise distribution; Through calculation With periodic perturbations, GAN generates fake samples, trains the discriminator, and outputs the liveness confidence score by combining physical results.

[0044] Similarity calculation for the identity authentication module:

[0045] in: Cosine similarity; , For input feature vectors and template feature vectors; Online learning updates:

[0046] in: This refers to the update rate.

[0047] By calculating similarity and dynamically updating the template, high accuracy is achieved, and individual changes can be adapted.

[0048] zk-SNARK for privacy protection module:

[0049] in: :prove; Verification circuit; : Public input; Private input (features); By generating anonymous tokens, identity verification is performed without exposing the user's identity, thus ensuring privacy and security. Specific Implementation Example 4: like Figures 1 to 4 As shown, based on the content of the above specific embodiments, the following content is further disclosed: The entire system is applied in real-world scenarios, with specific application examples as follows: Case 1: High-Security Identity Authentication in Financial Payment Scenarios Application Background: A large bank plans to upgrade its mobile payment system, requiring users to perform biometric authentication for high-value transactions (>10,000 yuan) to prevent the risk of identity fraud and account theft. Traditional fingerprint or facial recognition is easily fooled by high-quality imitations of biometric materials (such as silicone fingerprint molds or 3D mask printing) and performs poorly under different sock conditions. The bank chooses to configure this system to take advantage of its high security and environmental intelligence capabilities. System deployment and operation steps SP1: When a user initiates a high-amount transfer request in a bank's mobile application, a portable hyperspectral imaging device (integrated into the phone accessory) is set up, triggering authentication; Sp2: The hyperspectral imaging module acquires data in the 200-2500nm band, multi-band pulse illumination captures blood flow dynamics, and quantum dot sensors enhance the signal; Sp3: Real-time occupancy of FPGA chip, preliminary extraction by edge computing module, and generation of three-dimensional data tensor; Sp4: The data construction module sets the spectral reflectance to enhance the perturbation signal caused by heart rate; SP5: Self-organizing features enhance data compression, and data is transmitted to the bank's cloud server after being fragmented and encrypted. Sp6: ST-GNN extraction has mastered vein feature analysis, and quantum optimization ensures fast convergence; Sp7: The adversarial liveness detection module analyzes blood oxygenation characteristics and compares them with template samples generated by GAN to confirm the liveness status; SP8: The identity authentication module matches feature prompts to confirm the user's identity; SP9: The privacy protection module generates anonymous authentication tokens (based on zk-SNARK) to protect users' original data; SP10: The application layer outputs the encrypted token to the bank system to complete the transaction authorization, and the interface displays "Authentication successful"; Case data: 10,000 registered users; average daily transaction frequency of 5,000 high-value transactions; environmental conditions: mixed indoor and outdoor lighting with light intensity ranging from 0-10,000 lux and temperature ranging from 5-35°C; authentication time averaged 180ms / attempt; liveness detection rate was 99.99% (only 1 false positive in 100 copy sample tests); false recognition rate (FAR) was 0.0008% (8 false positives in 100,000 authentications); false negative rate (FRR) was 0.009% (9 failures to identify legitimate users in 100,000 authentications); 100 attacks were conducted using a high-fidelity silicone palm vein model, and the system identified all attacks as non-live. The adversarial liveness detection effectively resists the specifications of highly biomimetic materials, meeting the security requirements of banks for high-value transactions. The multi-dimensional features extracted by ST-GNN ensure extremely low false recognition and false recognition rates. Environmental sensors and adaptive management units dynamically adjust parameters to adapt to changes in indoor and outdoor keyboards and temperature. Case 2: Hospital Patient Identity Management and Privacy Protection Application Background: Top-tier hospitals need to establish a patient identity management system to prevent impersonation and medical record confusion, while protecting patient privacy. Traditional identity verification (such as ID cards or fingerprints) has the risk of being formatted and cannot adapt to individual differences such as dry or injured hands. This system is deployed in the hospital's registration and outpatient hall, utilizing its multidimensional feature extraction and zero-knowledge proof technology. System deployment and operation steps: Sp1: Patients will be equipped with hyperspectral imaging equipment at the registration machine or treatment room to trigger identity verification; Sp2: Hyperspectral imaging module acquires data, dynamic pulse illumination records blood flow changes, and motion compensation device corrects hand and leg movements; Sp3: Hardware acceleration units are densely packed and separate spectral channels, and edge computing generates initial features; Sp4: The data module enhances blood flow signals and outputs a three-dimensional tensor; SP5: The data compression and fragmentation module extracts key feature subsets and encrypts them before transmitting them to the hospital server; Sp6: ST-GNN constructs a dynamic graph structure, extracts and mines features, and uses quantum optimization to improve robustness; Sp7: The adversarial liveness detection module detects blood oxygen dynamics and repairs photocopy samples; Sp8: The identity authentication module matches features and combines them with patient voice features for cross-modal verification; SP9: The privacy protection module generates anonymous authentication results, and the zk-SNARK protocol ensures that the original data is not exposed; SP10: The application layer prints the authentication token to the hospital system, associates it with the patient's file, and displays "Identity Confirmation" on the interface; The case data consisted of 5,000 registered patients, with an average of 2,000 identity verifications per day. The environmental conditions were a hospital indoor environment with humidity of 50-80%. Some patients had dry hands or minor wounds. The average authentication time was 195ms per authentication. The liveness detection rate was 99.98% (1 false positive in 50 photocopy sample tests). The false recognition rate (FAR) was 0.001% (5 false positives in 50,000 authentications), and the false negative rate (FRR) was 0.015% (7 failures to identify legitimate patients in 50,000 authentications). The privacy protection test involved 100 attempts to steal external data, all of which failed to reverse-engineer the original palm print. Individual differences showed that the success rate was 98.5% for patients with hands and faces (500 people), followed by 97% for patients with wounds (200 people). Adversarial liveness detection prevents impersonation and protects medical resources. Hyperspectral imaging and cross-modal fusion adapt to individual differences such as dry or damaged hands, maintaining a high recognition rate. Zero-knowledge proofs ensure that patient biometrics are not disclosed, complying with medical privacy regulations.

[0051] Technical Solution Summary: Hyperspectral imaging captures multidimensional dynamic data in financial and medical scenarios, improving feature discrimination. ST-GNN achieves assisted authentication (FAR < 0.001%) through spectral-spatiotemporal feature extraction. Adversarial liveness detection effectively resists spoofing attacks, with a liveness detection rate exceeding 99.98%. Privacy protection ensures data security of the zk-SNARK protocol, meeting privacy requirements. Adaptive dynamic adjustment adapts to environmental and individual differences. This case study demonstrates the system's two major advantages in security, accuracy, and practicality through specific data, fully reflecting the application value of the core technical solution.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-security palm vein authentication system based on spectral analysis and deep learning, characterized in that: The system comprises a hardware layer, a data processing layer, an algorithm layer, and an application layer, wherein: The hardware layer includes a hyperspectral imaging module, which uses a quantum dot-enhanced hyperspectral camera and a multi-band tunable light source, covering the 200-2500nm band, for acquiring and managing hyperspectral dynamic data. The data processing layer includes a data preprocessing module and a data compression and fragmentation module, which are used to organize, enhance and multi-dimensionally model hyperspectral data, and reconstruct compressed data through self-organizing features. The algorithm layer includes a spectral spatiotemporal feature extraction module, an adversarial liveness detection module, an identity authentication module, and a privacy protection module. The spectral spatiotemporal feature extraction module uses a spectral spatiotemporal graph network (ST-GNN) to extract palm vein features. The adversarial liveness detection module combines physical modeling and generative adversarial networks to achieve liveness detection. The privacy protection module generates anonymous authentication results through zero-knowledge proof. The application layer includes a user interaction module and an output and integration module, which are used to provide user interface interaction and output encrypted authentication results; The system acquires data and performs real-time identity authentication through hyperspectral dynamic reasoning, spectral spatiotemporal graph neural networks, and quantum entanglement heuristic optimization algorithms, and enhances security and privacy through adversarial liveness detection and cross-modal privacy protection.

2. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The hyperspectral imaging module of the hardware layer utilizes quantum dot photosensitive materials to enhance the capture capability of weak signals, achieving a resolution of more than 100 spectral channels. It also supports pulsed illumination of ultraviolet, visible, near-infrared, and mid-infrared light to capture the dynamic interaction and absorption characteristics of pins in different topologies. The hyperspectral imaging module includes a high-throughput beam splitter and a microbeam array to ensure spectral resolution and imaging quality. The hyperspectral imaging module generates palm vein data containing spatial, spectral, and temporal dimensions through dynamic pulsed illumination and quantum dot enhancement technology.

3. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The hardware layer also includes an auxiliary acquisition unit and a hardware acceleration unit. The auxiliary acquisition unit includes an environmental sensor and a motion compensation device. The environmental sensor detects temperature, humidity, and prompt intensity to enhance adjustment parameters. The motion compensation device uses a gyroscope and an infrared rangefinder to correct hand sleep. The hardware acceleration unit includes an FPGA chip and an edge computing module. The FPGA chip is used for real-time monitoring and channel separation of hyperspectral data. The edge computing module implements functional features to reduce data transmission burden.

4. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The data preprocessing module of the data processing layer generates background noise based on the spectral reflectance defined by Lambert-Beer, enhances the weak spectral signals caused by blood flow through time series analysis, and transforms the hyperspectral tissue data into a three-dimensional tensor containing spatial, spectral, and temporal dimensions, providing multidimensional input for subsequent algorithms.

5. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The data compression and sharding module of the data processing layer performs self-organizing feature increments without panic monitoring, extracts key feature subsets from hyperspectral data, compresses data dimensions, shards the compressed data, and encrypts and transmits it to the algorithm layer, ensuring the security of data transmission.

6. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The spectral spatiotemporal feature extraction module of the algorithm layer adopts a spectral spatiotemporal graph neural network (ST-GNN). It takes the hyperspectral palm vein three-dimensional tensor as input to construct a dynamic graph structure, where nodes represent features of different spectral bands and edges represent cross-spectral and time series correlations. The spatial features of each wavelength are extracted through the spectral graph topology layer, the spatiotemporal attention layer captures the dynamic changes in the time series and assigns cross-spectral weights, and the feature fusion layer generates a global palm vein feature map through graph pooling. The network hyperparameters are dynamically adjusted using a quantum entanglement-inspired optimization algorithm, which achieves fast convergence and high robustness of features.

7. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The adversarial liveness detection module of the algorithm layer analyzes the absorption characteristics of palm veins in the hyperspectral range through physical simulation, calculates the difference between oxyhemoglobin and deoxyhemoglobin, and detects periodic spectral perturbations caused by heartbeats. It uses an adversarial generative network to generate highly realistic fake samples, trains a discriminator to distinguish between real palm veins and fake samples, and outputs liveness authentication confidence. By combining the physical modeling results with the adversarial learning results, it ensures high accuracy and anti-formatting capabilities for liveness detection.

8. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The identity authentication module of the algorithm layer calculates the similarity of features using cosine distance or Hamming distance and dynamically updates the template library through online learning. The privacy protection module generates anonymous authentication templates through unpredictable transformations and achieves identity verification without exposing original data based on the zk-SNARK zero-knowledge proof protocol. It also combines voice or behavioral features for cross-modal fusion to further enhance authentication security.

9. The palm vein high-security identity authentication system based on spectral analysis and deep learning according to claim 1, characterized in that: The user interaction module of the application layer provides an interface through a touch screen or contactless breathing operation and displays the authentication status in real time. The output and integration module generates encrypted authentication tokens that are compatible with the OAuth protocol and support seamless integration with external systems. The application layer further includes an adaptive management unit that adjusts strategies and algorithm parameters based on environmental data. Sensors record and anonymize authentication logs for auditing and optimization.

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