In hand-based biometrics, fingerprint, finger vein, finger knuckle print, palm print, palm vein, dorsal hand vein, and hand shape are the traits that are getting much attention. However, finger shape (FS), a forgettable trait, has not been studied specifically for identification purposes. In this work, we explore this content as a complement to the hand-based biometrics. Firstly, we annotate the FS on a publicly available finger vein dataset as the ground truth for finger semantic segmentation. Then we explore the finger semantic segmentation task on the annotated data and propose a lightweight network, namely FinSeg-Net (finger segmentation network). Finally, we conduct the FS authentication experiment based on four matching methods; experimental results show that the FS traits can achieve identity authentication. This work is the first study for FS biometrics specifically, and built the first FS dataset, which will be accessed via: https://github.com/SCUT-BIP-Lab/FinSeg.

Hand-based multimodal biometrics have attracted significant attention due to their high security and performance. However, existing methods fail to adequately decouple various hand biometric traits, limiting the extraction of unique features. Moreover, effective feature extraction for multiple hand traits remains a challenge. To address these issues, we propose a novel method for the precise decoupling of hand multimodal features called 'Normalized-Full-Palmar-Hand' and construct an authentication system based on this method. First, we propose HSANet, which accurately segments various hand regions with diverse backgrounds based on low-level details and high-level semantic information. Next, we establish two hand multimodal biometric databases with HSANet: SCUT Normalized-Full-Palmar-Hand Database Version 1 (SCUT_NFPH_v1) and Version 2 (SCUT_NFPH_v2). These databases include full hand images, semantic masks, and images of various hand biometric traits obtained from the same individual at the same scale, totaling 157,500 images. Third, we propose the Full Palmar Hand Authentication Network framework (FPHandNet) to extract unique features of multiple hand biometric traits. Finally, extensive experimental results, performed via the publicly available CASIA, IITD, COEP databases, and our proposed databases, validate the effectiveness of our methods.

Brainprint has emerged as a promising biometrics modality due to its advantages of high security and user subjectivity. Currently, Brainprint recognition faces two fundamental limitations: (1) the inherent susceptibility of EEG signals to physiological artifacts, and (2) the limited capacity of extracting reliable identity-relevant features. To address the above limitations, we propose WST-CAA, a novel Brainprint recognition framework that combines a cascaded preprocessing and wavelet scattering transform pipeline (PWST) with a channel-axial attention dense network (CAADN). The proposed WST-CAA excels in denoising and identity feature extraction, effectively capturing channel-spatial-temporal correlations while suppressing cross-dimensional noise. Extensive experiments on the publicly available datasets, FACED and DEAP, demonstrate that WST-CAA outperforms baseline models, achieving 99.48% accuracy on DEAP and 97.46% on FACED while maintaining state-of-the-art performance across both small and large-scale EEG datasets. The relevant code is available at:https://github.com/jhuangscnu/BrainPrint_WST-CAA.

Full-view finger vein (FV) biometrics systems capture multiple FV images of the presented finger ensuring that the entire surface of the finger is covered. Existing full-view FV systems suffer from three common problems: large device size, high cost for multi-camera system, and sub-optimal illumination in the recorded FV images. To address the problem of device size, we propose a novel Mirror-based Full-view FV (MFFV) capture device. The MFFV device has a compact size by using mirror-reflection approach. We reduce the cost of the device by using low-cost components, in particular, consumer-grade cameras. To address the problems of lower-quality images captured by such cameras and obtain optimally illuminated FV images, we propose a two-step approach. The first step is a Multi-illumination Intensities FV (MIFV) capture strategy, which capture the FV image set with varying illumination intensities. In the second step, a FV illumination adaptation (FVIA) algorithm is proposed to select the optimally illuminated FV image from the MIFV image set. Using the proposed MFFV device, we collect a comprehensive dataset, namely MFFV dataset, along with reproducible baseline FV authentication results for both single-view and full-view FV. Our experimental results demonstrate that the MIFV capture strategy as well as the FVIA algorithm can effectively improve the authentication performance, and that the full-view FV authentication is significantly superior than the single-view FV authentication. The source-code and dataset for reproducing our experimental results are publicly available. The code and the license for MFFV-N dataset can be accessed at:https://github.com/SCUT-BIP-Lab/MFFV.

As a biometric trait drawing increasing attention, finger vein (FV) has been studied from many perspectives. One promising new direction in FV biometrics research is full-view FV biometrics, where multiple images, covering the entire surface of the presented finger, are captured. Full-view FV biometrics presents two main problems: increased computational load, and low performance-to-cost ratio for some views/regions. Both problems are related to the inherent redundancy in vascular information available in full-view FV images. In this work, we address this redundancy issue in full-view FV biometrics. Firstly, we propose a straightforward FV redundancy analysis (FVRA) method for quantifying the information redundancy in FV images. Our analysis shows that the redundancy ratio of full-view FV images is up to 83%-87%. Then, we propose a novel feature extraction model, named FV dynamic Transformer (FDT), whose architecture is configured based on the redundancy analysis results. The FDT focuses on both local (single-view) information as well as global (full view) information at different processing stages. Both stages provide the advantage of de-redundancy and noise avoidance. Additionally, the end-to-end architecture simplifies the full-view FV biometrics pipeline by enabling the direct, simultaneous processing of multiple input images, thus consolidating multiple steps into one. A series of rigorous experiments is conducted to evaluate the effectiveness of the proposed methods. Experimental results show that the proposed FDT achieves state of the art authentication performance on the MFFV-N dataset, yielding an EER of 0.97% on the development set and an HTER of 1.84% on the test set under the balanced protocol and EER criterion. The cross-domain generalization capability of FDT is also demonstrated on the LFMB-3DFB dataset, where it achieves an EER of 7.24% and an HTER of 7.34% under the same protocol and criterion. Code for the proposed methods can be access via: https://github.com/SCUT-BIP-Lab/FDT.

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.

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.

Finger contains several discriminative biometric traits, including fingerprint, finger vein, finger knuckle, and finger shape, which are complementary in identity information. However, in most current researches and practical applications, only a single or several traits are utilized, which are prone to unsatisfactory recognition performance and easy forgery. Our work is 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 designed. To the best of our knowledge, it is the first biometric imaging system that can capture almost all finger-based traits. With this 3D finger imaging system, we scanned numerous fingers, acquiring their external skin images and internal vein images from 6 different views. Then 3D finger models with skin and vein textures are reconstructed by space carving, mesh regularization, and texture mapping algorithms. Secondly, we establish a benchmark dataset, namely the Large- scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). LFMB-3DFB contains 695 fingers, and each finger is captured 10 times. Then, 6 finger skin images and 6 finger vein images are obtained for each acquisition, and final 83,400 images and 6,950 3D finger models are obtained. Besides, we designed a more rigorous and comprehensive evaluation protocol for both identification and verification tasks. Finally, we designed corresponding baselines for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion. Rigorous experiments have been conducted to verify the significance and usefulness of the proposed LFMB-3DFB.