Palm Vein Recognition Research Articles

Decoupling visual and identity features for adversarial palm-vein image attack

Palm-vein has been widely used for biometric recognition due to its resistance to theft and forgery. However, with the emergence of adversarial attacks, most existing palm-vein recognition methods are vulnerable to adversarial image attacks, and to the best of our knowledge, there is still no study specifically focusing on palm-vein image attacks. In this paper, we propose an adversarial palm-vein image attack network that generates highly similar adversarial palm-vein images to the original samples, but with altered palm-identities. Unlike most existing generator-oriented methods that directly learn image features via concatenated convolutional layers, our proposed network first maps palm-vein images into multi-scale high-dimensional shallow representation, and then develops attention-based dual-path feature learning modules to extensively exploit diverse palm-vein-specific features. After that, we design visual-consistency and identity-aware loss functions to specially decouple the visual and identity features to reconstruct the adversarial palm-vein images. By doing this, the visual characteristics of palm-vein images can be largely preserved while the identity information is removed in the adversarial palm-vein images, such that high-aggressive adversarial palm-vein samples can be obtained. Extensive white-box and black-box attack experiments conducted on three widely used databases clearly show the effectiveness of the proposed network.

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

Optimizing CNNs for Contactless Palmprint Recognition

Palm printing and palm vein recognition are the newer spheres within the already developed biometrics sector. Although many time-honored techniques have been offered and seen successful implementation over the last twenty years, the deep learning methods still lack all-round development in the palmprint and palm vein recognition. This research intends to study further the strength of deep learning on palmiets and palm vein recognition via 2D and 3D. We performed thorough examination of 17 known convolutional neural networks (CNNs) utilizing multiple databases, e.g., one 3D palmprint database, five 2D palmprint database and two palm vein databases. Our trials cover various network architectures, learning rates, and layer configurations, taking into account not only single mode data but also mixed mode data simultaneously. Results prove that CNNs of classic format show good recognition capabilities, and recent models with improved accuracy show even better achievements. One of the classic CNNs that stands out is Efficient Net. If the recognition accuracy is evaluated, this is the top performer. On the other hand, even though the classic forms of CNNs are fairly good in recognizing various types of tumors, their accuracy is still lower than the traditional methods. “Abstract” is a necessary section in a research paper. It may be constructed by gathering main points (summary) from each section of the research paper.

A Review on Palm Vein Recognition using Deep Neural Network

The expansion of the internet has significantly in- creased the use of internet data, which necessitates security through unique identity. Traditional security mechanisms, such as user credentials and personal identification numbers (PINs), failed to meet user’s demands. Hence a unique identification mechanism is required to offer excellent security for the data. Bio- metrics plays a vital role in today’s authentication and recognition in various situations. Biometrics is concerned with the study of specific behavioral and physical characteristics. Fingerprint, face, iris/ retinal, voice, and vascular authentication are some of the biometric modalities that are used for identification. In this paper, different procedures involving palm vein recognition and authentication are discussed

Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition.

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

Palm Vein Recognition Under Unconstrained and Weak-Cooperative Conditions

Palm Vein Recognition Under Unconstrained and Weak-Cooperative Conditions

Double Gabor-filter palm vein recognition network

Double Gabor-filter palm vein recognition network

Fusion recognition of palmprint and palm vein based on modal correlation.

Biometric authentication prevents losses from identity misuse in the artificial intelligence (AI) era. The fusion method integrates palmprint and palm vein features, leveraging their stability and security and enhances counterfeiting prevention and overall system efficiency through multimodal correlations. However, most of the existing multi-modal palmprint and palm vein feature extraction methods extract only feature information independently from different modalities, ignoring the importance of the correlation between different modal samples in the class to the improvement of recognition performance. In this study, we addressed the aforementioned issues by proposing a feature-level joint learning fusion approach for palmprint and palm vein recognition based on modal correlations. The method employs a sparse unsupervised projection algorithm with a "purification matrix" constraint to enhance consistency in intra-modal features. This minimizes data reconstruction errors, eliminating noise and extracting compact, and discriminative representations. Subsequently, the partial least squares algorithm extracts high grayscale variance and category correlation subspaces from each modality. A weighted sum is then utilized to dynamically optimize the contribution of each modality for effective classification recognition. Experimental evaluations conducted for five multimodal databases, composed of six unimodal databases including the Chinese Academy of Sciences multispectral palmprint and palm vein databases, yielded equal error rates (EER) of 0.0173%, 0.0192%, 0.0059%, 0.0010%, and 0.0008%. Compared to some classical methods for palmprint and palm vein fusion recognition, the algorithm significantly improves recognition performance. The algorithm is suitable for identity recognition in scenarios with high security requirements and holds practical value.

Attention Label Learning to Enhance Interactive Vein Transformer for Palm-Vein Recognition

Attention Label Learning to Enhance Interactive Vein Transformer for Palm-Vein Recognition

Contactless Biometric Based on Palm Vein Recognition Using Wavelet and Local Line Binary Patterns

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.

Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition

At present, COVID-19 is posing a serious threat to global human health. The features of hand veins in infrared environments have many advantages, including non-contact acquisition, security, privacy, etc., which can remarkably reduce the risks of COVID-19. Therefore, this paper builds an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. The gesture contours are extracted and input into an improved convolutional neural network for gesture recognition. The hand is tracked based on key point detection. Because the hand gesture commands are randomly generated and the hand vein features are extracted from the infrared environment, the anti-counterfeiting performance is obviously improved. In addition, hand tracking is conducted after gesture recognition, which prevents the escape of the hand from the camera view range, so it ensures that the hand used for palmvein recognition is identical to the hand used during gesture recognition. The experimental results show that the proposed gesture recognition method performs satisfactorily on our dataset, and the hand tracking method has good robustness.

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

Identity Recognition System Based on Multi-Spectral Palm Vein Image

A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value.

Learning modality-invariant binary descriptor for crossing palmprint to palm-vein recognition

Palmprint has shown promising potential for biometrics recognition due to its excellent contactless and hygienic properties. However, most existing methods focus only on feature learning of unimodal palmprint images leading to the challenge of palmprint recognition crossing different modalities. In this paper, we propose a modality-invariant binary features learning (MIBFL) method for crossing palmprint to palm-vein recognition, where palm images are captured under visible and invisible near-infrared illumination, respectively. We first map the multiple modal palm images into their high-dimensional alignment representation to reduce the impact of misalignment and noise between different image modalities. Then, we simultaneously learn the discriminative features from different modalities of alignment images via matrix factorization by enforcing the orthogonal and balanced constraints. Lastly, we jointly learn a pair of encoding functions to project multi-modal palm features into the common binary feature descriptor for crossing palmprint to palm-vein recognition. Experimental results on the widely used PolyU multi-spectral palmprint database are presented to demonstrate the effectiveness of the proposed MIBFL method.

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one’s lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.

LBP-Based Contactless Palm Vein Authentication

Palm Vein Recognition is one among the biometric authentication methods. It works by recognizing the unique patterns of veins in the palms of the people. In palm vein recognition, the near-infrared light is illuminated into the palm emitted by the palm scanner. This near-infrared light is absorbed by the deoxygenated blood flowing through the veins which reduces its ability to reflect back the light. This causes the veins to appear as black patterns and is captured as an image by the scanner. These images are processed using various algorithms and compared with the data in the database and authenticates the individuals.

A Lightweight Neural Network for Palm Vein Recognition

Due to the advantages of lightweight networks such as efficiency, portability, and fast inference, we propose a palm vein recognition network based on lightweight neural networks. The network consists of three stages combining Blocks of ShuffleNetV2, Blocks of MobileNetV3, and MBConv of EfficientNet. The number of each stage in the network is chosen based on the baseline parameters and computational effort, and the expansion factors are re-analyzed and selected. The performance of the proposed network is compared to the baseline with a 16.78% reduction in error rate and a 10.91% compression of parameters. The experimental results show that the proposed palm vein recognition network is effective, which can potentially contribute to the development of more accurate, portable and reliable biometric systems.

CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

Contactless Palm Vein Recognition System with Integrated Learning Approach System

Contactless Palm Vein Recognition System with Integrated Learning Approach System

Label Enhancement-Based Multiscale Transformer for Palm-Vein Recognition

Vein biometrics is a high security and privacy preserving identification technology that has received increasing attentions. Although deep neural networks (DNNs), such as convolutional neural network (CNN), have been investigated for vein recognition and achieved a significant improvement in accuracy, they still fail to model long-range pixel dependencies in an image. Moreover, their performance is limited because the one-hot label vector employed for training may ignore the relevance among labels. To address these problems, we propose LE-MSVT, a Label Enhancement based Multi-Scale Vein Transformer for palm-vein recognition in this paper. First, we propose a multi-scale vein transformer (MSVT) to learn robust and multi-scale features, which consists of a convolutional block that captures the local information and a self-attention block that extracts scale dependencies among images with different scales. Second, to capture the relevance among labels, we explore a graph convolutional network based label enhancement (GCNLE) approach to recover the realistic label distribution for vein classification improvement. GCNLE exploits a multi-layer perception to learn an effective label correlation matrix for extracting the relation information between an input image and multiple training images from different classes. The label distribution vector is generated and then combined with the one-hot label to compute a realistic label distribution of the input image. Finally, we apply GCNLE to MSVT to obtain LE-MSVT, which is trained in an end-to-end way to further improve the feature representation capacity of MSVT classifier. We conduct extensive experiments in terms of MSVT performance and LE-MSVT improvements on three public palm-vein databases, and the experimental results show that the resulting MSVT outperforms other vein identification approaches and achieves the best performance among existing approaches, and GCNLE can greatly improve the performance of MSVT among other deep learning based classifiers.