Finger vein, as an intrinsic trait, has received increasing attention from academia and industry for its liveness detection ability and high security. However, the principle of vein imaging and its contact-less mode results in low contrast of finger vein image, where especially the vein texture is susceptible to factors, such as scattering in skin and tissue, ambient illumination, and finger posture variation. To extract discriminative features from low contrast images, in this article, we propose an attention mechanism, namely, joint attention (JA) module, which enables dynamic adjustment and information aggregation in the spatial and channel dimensions of feature maps to focus on fine-grained details, thus enhancing the contribution of vein patterns to extract identity features. Besides, we embed a generalized mean (GeM) pooling layer into our network to reduce the dimensionality of feature maps and output a compact and highly expressive feature representation. Finally, we build a new finger vein authentication architecture, called JA finger vein network (JAFVNet), with competitive experimental results on multiple public and self-built datasets: 0.35% EER for SDUMLA-HMT, 0.08% EER for MMCBNU-6000, 0.34% EER for FV-USM, and 0.49% EER for FV-SCUT. Numerous ablation experiments further illustrate the effectiveness of each component in the network.

In recent years, deep learning-based finger vein (FV) authentication has attracted the attention of biometric researchers and achieved breakthrough results. Previously, convolutional neural networks (CNNs) were the most commonly used deep learning-based methods for FV authentication. Recently, the vision Transformer (ViT)-based method has started getting attention from the research community due to its excellent performance in many computer vision tasks. In this article, we delve into ViTs and propose a novel model, FV Transformer (FVT), for FV authentication. The FVT consists of four key modules: 1) the conditional position embedding, which is capable of dynamically generating position codes according to the input FV tokens; 2) the weight-shared expanded multilayer perceptron (EMLP), which helps to extract richer and more robust token information; 3) the local information-enhanced feedforward network (FFN), which enhances the ability of local information extraction; and 4) the expansion-less mechanism (ELM) for aggregating adjacent FV tokens, which implements the pyramid structure, and hence, the multilevel feature extraction capability is introduced to the Transformer architecture, which originally focuses on global information. To fully validate the performance and generalization of FVT, experiments were conducted on nine publicly available FV datasets. The effectiveness of each key module of FVT is demonstrated in the ablation experiments. Also, the comparative experiments show that the FVT outperforms several baseline Transformer models and achieves competitive performance when compared with the state-of-the-art (SOTA) FV authentication methods.

Finger vein biometrics is becoming an important source of human authentication due to its advantages in terms of liveness detection, high security, and user convenience. Although there exist a lot of deep learning-based methods for finger vein authentication, they only extract features from finger vein images in the spatial domain and may lose some important information that is present in other domains, such as the frequency domain. Motivated by this conjecture and the remarkable performance of image feature extraction in the frequency domain, this work explores a method capable of extracting finger vein features in both the spatial and frequency domains. Therefore, the features extracted from different domains can complement each other. In addition, we propose a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication. FVFSNet is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain processing module (SDPM), and (3) the frequency-spatial coupling module (FSCM). The FDPM is used to extract the finger vein features present in the frequency domain, which is mainly composed of the frequency-spatial domain transformation and the frequency domain convolution layer. The SDPM is used to extract the finger vein features present in the spatial domain, which is mainly composed of convolution layers with an efficient design. The FSCM is used to couple the features extracted from the FDPM and SDPM, which is mainly composed of the channel and spatial attention mechanisms. To validate our conjecture and the performances of FVFSNet, extensive experiments are conducted on nine commonly used publicly available finger vein datasets. Experimental results show that the frequency domain constitutional neural network has a surprising effect on finger vein authentication, and the proposed FVFSNet achieves the state-of-the-art performance with the advantages of lightweight and low computational cost.

Fingers contain various discriminative biometric traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on, which are complementary in identity information. However, only one or a few traits are used in most current research and practical applications, while others are ignored, resulting in degraded recognition performance and vulnerability to forgery. In this paper, we make the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is proposed. To the best of our knowledge, it is the first biometric imaging system capable of capturing almost all finger-based traits. We scanned numerous fingers with this 3D finger imaging system, obtaining external skin images and internal vein images from 6 different views. The proposed 3D finger reconstruction and texture mapping algorithms are then used to generate 3D finger models with skin and vein textures. Second, we establish a benchmark dataset, namely the Large-scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). The LFMB-3DFB contains 695 fingers, and each finger is acquired 10 times, yielding 6 finger skin images and 6 finger vein images for a total of 83,400 images and 6,950 3D finger models. Finally, we design a more scientific and comprehensive evaluation protocol to conduct extensive experimental research and analysis on this database for both subject-independent verification and subject-independent close-set identification tasks. Comprehensive and rigorous experiments for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion on LFMB-3DFB are carried out, and excellent results are achieved. The LFMB-3DFB database will be released at https://github.com/SCUT-BIP-Lab/LFMB-3DFB to promote 3D finger multi-biometric research using cutting-edge imaging techniques.

More palm features, such as veins and shapes obtained from an enlarged contactless palm vein region of interest (ROI), have been shown to improve recognition performance. However, a few efforts have been made to adequately utilize these features for mining identity information. To address this issue, we propose a Region-Specific Network (RSNet) for contactless palm vein authentication. Our RSNet is a dual-branch structure for global and local feature extraction. Firstly, a Region-based Local feature Enhancement Block (RLEB) is proposed at the local branch to extract region-specific features. In the RLEB, the intermediate feature maps are divided into three asymmetrical patches based on the physiological characteristics of palm vein and palm shape for extracting diversified features, enhancing the local feature representation. Then, a Multi-scale Aggregation Block (MAB) is proposed that efficiently aggregates multi-scale features at a more granular level. Furthermore, to guide the global and local branches in learning complementary feature aspects, a difference loss is introduced to apply a soft subspace orthogonality constraint between the global and local vectors during training. The global branch is designed to assist the learning process of local features, without being adopted for inference. Extensive experiments have demonstrated the effectiveness and superiority of our method, and the RSNet achieves new State-Of-The-Art (SOTA) authentication performance on seven public contactless palm vein databases in the open-set scenario.