Finger Vein Recognition Research Articles

Finger Vein Recognition Using DenseNet with a Channel Attention Mechanism and Hybrid Pooling

This paper proposes SE-DenseNet-HP, a novel finger vein recognition model that integrates DenseNet with a squeeze-and-excitation (SE)-based channel attention mechanism and a hybrid pooling (HP) mechanism. To distinctively separate the finger vein patterns from their background, original finger vein images are preprocessed using region-of-interest (ROI) extraction, contrast enhancement, median filtering, adaptive thresholding, and morphological operations. The preprocessed images are then fed to SE-DenseNet-HP for robust feature extraction and recognition. The DenseNet-based backbone improves information flow by enhancing feature propagation and encouraging feature reuse through feature map concatenation. The SE module utilizes a channel attention mechanism to emphasize the important features related to finger vein patterns while suppressing less important ones. HP architecture used in the transitional blocks of SE-DenseNet-HP concatenates the average pooling method with a max pooling strategy to preserve both the most discriminative and contextual information. SE-DenseNet-HP achieved recognition accuracy of 99.35% and 93.28% on the good-quality FVUSM and HKPU datasets, respectively, surpassing the performance of existing methodologies. Additionally, it demonstrated better generalization performance on the FVUSM, HKPU, UTFVP, and MMCBNU_6000 datasets, achieving remarkably low equal error rates (EERs) of 0.03%, 1.81%, 0.43%, and 1.80%, respectively.

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

Multi scale-aware attention for pyramid convolution network on finger vein recognition

In recent years, biometrics has been the most popular style of personal identification. The finger vein is an intrinsic and stable trait, and with the ability to detect liveness, it receives academic and industry attention. However, convolution neural networks (CNNs) based finger vein recognition generally can only cover a small input region by using small kernels. Hence, the performance is poor, facing low-quality finger vein images. It is a challenge to effectively use the critical feature of multi-scale for finger veins. In this article, we extract multi-scale features via pyramid convolution. We propose scale attention, namely, the scale-aware attention (SA) module, which enables dynamic adjustment of the weight of each scale to information aggregation. Utilize the complementation of different scale detail features to enhance the discriminativeness of extracted features, thus improving the finger vein recognition performance. In order to verify the present method’s efficiency, we carried out experiments on two public data sets and one internal data, and the wide range of experimental results proves the proposed method’s effectiveness.

Finger vein recognition based on efficient channel attention and GhostNet

Finger vein recognition based on efficient channel attention and GhostNet

Finger Vein Recognition Based on ResNet With Self-Attention

Finger Vein Recognition Based on ResNet With Self-Attention

A secure finger vein recognition system using WS-progressive GAN and C4 classifier

A secure finger vein recognition system using WS-progressive GAN and C4 classifier

A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics

Structural features are capable of effectively capturing the overall texture variations in images. However, in locally prominent areas with visible veins, other characteristics such as directionality, convexity–concavity, and curvature also play a crucial role in recognition, and their impact cannot be overlooked. This paper introduces a novel approach, the histogram of variable curvature directional binary statistical (HVCDBS), which combines the structural and directional features of images. The proposed method is designed for extracting discriminative multifeature information in vein recognition. First, a multidirection and multicurvature Gabor filter is introduced for convolution with vein images, yielding directional and convexity–concavity information at each pixel, along with curvature information for the corresponding curve. Simultaneously incorporating the original image feature information, these four aspects of information are fused and encoded to construct a variable curvature binary pattern (VCBP) with multifeatures. Second, the feature map containing multifeature information is blockwise processed to build variable curvature binary statistical features. Finally, competitive Gabor directional binary statistical features are combined, and a matching score‐level fusion scheme is employed based on maximizing the interclass distance and minimizing the intraclass distance to determine the optimal weights. This process fuses the two feature maps into a one‐dimensional feature vector, achieving an effective representation of vein images. Extensive experiments were conducted on four widely utilized vein databases, and the results indicate that the proposed algorithm, compared with solely extraction of structural features, achieved higher recognition rates and lower equal error rates.

Double Branch Lightweight Finger Vein Recognition based on Diffusion Model

Double Branch Lightweight Finger Vein Recognition based on Diffusion Model

Dual-channel heterogeneous network for finger vein recognition based on transfer learning and improved coordinate attention

Dual-channel heterogeneous network for finger vein recognition based on transfer learning and improved coordinate attention

Dual-channel heterogeneous network for finger vein recognition based on transfer learning and improved coordinate attention

Dual-channel heterogeneous network for finger vein recognition based on transfer learning and improved coordinate attention

Attention BLSTM-Based Temporal-Spatial Vein Transformer for Multi-View Finger-Vein Recognition

Attention BLSTM-Based Temporal-Spatial Vein Transformer for Multi-View Finger-Vein Recognition

A Finger Vein Liveness Detection System Based on Multi-Scale Spatial-Temporal Map and Light-ViT Model.

Prosthetic attack is a problem that must be prevented in current finger vein recognition applications. To solve this problem, a finger vein liveness detection system was established in this study. The system begins by capturing short-term static finger vein videos using uniform near-infrared lighting. Subsequently, it employs Gabor filters without a direct-current (DC) component for vein area segmentation. The vein area is then divided into blocks to compute a multi-scale spatial-temporal map (MSTmap), which facilitates the extraction of coarse liveness features. Finally, these features are trained for refinement and used to predict liveness detection results with the proposed Light Vision Transformer (Light-ViT) model, which is equipped with an enhanced Light-ViT backbone, meticulously designed by interleaving multiple MN blocks and Light-ViT blocks, ensuring improved performance in the task. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT model was verified to be 99.63% on a self-made living/prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model is made lightweight.

VPCFormer: A transformer-based multi-view finger vein recognition model and a new benchmark

In the past decade, finger vein authentication garners significant interest. However, most existing databases and algorithms predominantly focused on single-view finger vein recognition. The current projection of vein patterns actually maps a 3D network topology into a 2D plane, which inevitably leads to 3D feature loss and topological ambiguity in 2D images. Additionally, single-view based methods are sensitive to finger rotation and translation in practical applications. So far, there are currently few dedicated studies and public databases on multi-view finger vein recognition. To address these issues, we first establish a benchmark for future research by constructing the multi-view finger vein database, named Tsinghua Multi-View Finger Vein-3 Views (THUMVFV-3V) Database , which is collected over two sessions. THUMVFV-3V provides three types of Regions of Interest (ROIs) and includes unified preprocessing operations, catering to the majority of existing methods. Furthermore, we propose a novel Transformer-based model named Vein Pattern Constrained Transformer (VPCFormer) for multi-view finger vein recognition, primarily composed of multiple Vein Pattern Constrained Encoders (VPC-Encoders) and Neighborhood-Perspective Modules (NPMs). Specifically, the VPC-Encoder incorporates a novel Vein Pattern Attention Module (VPAM) and an Integrative Feed-Forward Network (IFFN). Motivated by the fact that the strong correlations veins exhibit across different views, we devise the VPAM. Assisted by a vein mask, VPAM is meticulously designed to exclusively extract intra- and inter-view dependencies between vein patterns. Further, we propose IFFN to efficiently aggregate the preceding attention and contextual information of VPAM. In addition, the NPM is utilized to capture the correlations within a single view, enhancing the final multi-view finger vein representation. Extensive experiments demonstrate the superiority of our VPCFormer. The THUMVFV-3V database is available at https://github.com/Pengyang233/THUMVFV-3V-Database.

Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance.

Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition

ABSTRACT In information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.

Finger vein recognition with cancelable template-based partial mutual encryption

Finger vein recognition with cancelable template-based partial mutual encryption

Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System.

Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs' high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model's performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance.

Raspberry PI-Based Finger Vein Recognition System Using PCA NET

Despite simultaneously ignoring the intensity distribution that is formed by the finger tissue and, in some instances, processing it as background noise, the majority of finger vein feature extraction algorithms achieve satisfactory performance due to their ability to represent texture. This project makes use of two- directional two-dimensional Fisher Principal Component Analysis, also known as (2D) 2 FPCA, for feature extraction. This type of "noise" is presented as a novel soft biometric trait for improving finger vein recognition performance. In order to demonstrate that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition, a comprehensive analysis of the finger vein imaging principle and the characteristics of the image are first presented.

Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the vein patterns in fingers, and it has the benefit of being difficult to counterfeit. Due to its high level of security, durability, and performance history, finger vein recognition captures our attention as one of the most significant authentication methods available today. Using a mixed deep learning approach, we investigate the challenge of identifying the finger vein sensor model. Thus far, we use Traditional LSTM architectures for this biometric modality. This work also suggests a brand-new hybrid architecture that shines due to its compactness and a merging with the LSMT layer to be taught. In the experiment, original samples as well as the region of interest data from eight freely available FV-USM datasets are employed. The standard LSTM-based strategy is preferable and produced better outcomes, as seen by the comparison with the earlier approaches. Moreover, the results show that the hybrid CNN and LSTM networks may be used to improve vein detection performance.

Finger Vein Recognition Technology: Principles, Applications, and Future Prospects

Finger vein recognition technology is a biometric identification technique that analyzes the vein patterns in the fingers to authenticate individuals. This paper aims to provide a comprehensive overview of the principles, applications, and future prospects of finger vein recognition technology. Firstly, the unique biological characteristics and advantages of finger vein recognition will be explored. Secondly, the working principle and key techniques of the finger vein recognition system will be introduced, covering image acquisition, feature extraction, and matching algorithms. Subsequently, the extensive applications of finger vein recognition technology in personal identity authentication, financial payments, information security, and other domains will be discussed. Finally, the future development of finger vein recognition technology will be forecasted, addressing potential challenges and solutions, as well as its potential applications in broader domains.