Deep neural networks have recently achieved promising performance in the vein recognition task and have shown an increasing application trend. However, they are prone to adversarial attacks by adding imperceptible perturbations to the input, resulting in incorrect recognition. To address this issue, we propose a novel defense model named MsMemoryGAN, which aims to filter the perturbations from adversarial samples before recognition. First, we design a multiscale memory autoencoder (MsMemoryAE) to achieve high-quality reconstruction, where the memory module (MM) within it is capable of learning the detailed patterns of normal samples at different scales. Second, to overcome the limitations of handcrafted similarity metrics, we propose an MM with learnable similarity (LSMM), which retrieves the most relevant memory items to purify the input feature. Finally, the perceptual loss and adversarial loss are integrated with the pixel loss to further enhance the quality of the reconstructed image. During the training phase, the MsMemoryGAN learns to reconstruct the input by merely using fewer prototypical elements of the normal patterns recorded in the memory. At the testing stage, given an adversarial sample, the MsMemoryGAN retrieves its most relevant normal patterns in MMs for reconstruction. Perturbations in the adversarial sample are usually not reconstructed well, resulting in adversarial purification. We conduct extensive experiments on two public vein datasets under different adversarial attack methods to evaluate the performance of the proposed approach. The experimental results show that our approach removes a wide variety of adversarial perturbations, allowing vein classifiers to achieve the highest recognition accuracy.

Owing to its inherent attributes of high security, privacy preservation, and liveness detection, vein recognition has garnered significant attention, with deep learning (DL) models prevailing in the field. In particular, Mamba, a recent DL architecture showing robust feature representation with linear computational complexity, has been applied successfully for visual tasks. However, Vision Mamba captures long-distance feature dependencies but deteriorates local feature details. Besides, manually designing Mamba architecture based on human prior knowledge is very time-consuming and error-prone. To address these limitations, we propose a hybrid network structure named Global-local Vision Mamba (GLVM) to learn both local correlations and global dependencies within images for comprehensive vein feature representation. Second, we design a Multi-head Mamba to learn the dependencies along different directions, so as to improve the feature representation of Vision Mamba. Third, to learn complementary features, we propose a ConvMamba block consisting of three branches: Multi-head Mamba branch (MHMamba), Feature Iteration Unit branch (FIU), and Convolutional Neural Network (CNN) branch, with FIU aiming to fuse convolutional local features with Mamba global representations. Finally, we propose a Global-local Alternate Neural Architecture Search (GLNAS) method, which alternately searches for the optimal architecture of GLVM through weight entanglement strategy and evolutionary algorithm. We have carried out rigorous experiments on five public vein datasets to assess performance. Our approach achieves the highest 96.84%, 99.63%, 95.73%, 99.72%, 99.14% accuracies and the lowest 0.27%, 0.07%, 0.48%, 0.07%, 0.12% EER among all existing approaches on five public vein datasets, which demonstrates that our approach is capable of learning more complete features than existing approaches. In addition, the visual assessment experiments also show that our approach extracts more global vein architecture and local vein detail for recognition.

Vein recognition technology offers high security and privacy as an advanced biometric identification method. While deep learning techniques have achieved state-of-the-art performance in vein recognition due to their powerful pattern recognition capabilities, the Gated Recurrent Unit (GRU), a simplified version of LSTM, still faces limitations: 1) inability to process sequence information in parallel, leading to inefficient training; 2) loss of sensitivity to local features crucial for pattern recognition, despite excelling at modeling long-distance dependencies. To address these issues, we propose WTxGRN, a Wavelet Transform-based extended Gated Recurrent Network, which simultaneously extracts global and local features and supports parallel sequence processing. Specifically, we modify the GRU memory structure to enable parallel training and enhance feature representation through exponential gating and stabilization techniques, resulting in an extended GRU architecture called xGRU. We integrate xGRU into a wavelet transform-based residual backbone to form the xGRU Block. By incorporating a wavelet convolution branch and two Mixer Modules, we facilitate multi-scale feature extraction and fusion, enhancing vein recognition robustness and yielding the WTxGRU Block. Stacking these blocks constructs the WTxGRN. Furthermore, we present Spiking WTxGRN, an energy-efficient spiking version of WTxGRN, pioneering the application of spiking neural networks in vein recognition. Spiking WTxGRN offers high energy efficiency while maintaining excellent recognition performance, making it suitable for real-time vein recognition tasks. Extensive experiments on three public palm vein datasets demonstrate that our methods outperform state-of-the-art models across multiple benchmarks, achieving superior performance.

Palm vein recognition has attracted recently wide attention thanks to its robust feature representation and high accuracy. Despite advancements in the literature, however, existing solutions suffer from the following issues: 1) Insufficient large-scale data for deep learning-based recognition of vein biometrics, resulting in decreased generalization performance and model accuracy. 2) Lack of methods based on machine learning convolutional neural networks capable of capturing the global receptive field for vein biometric recognition. In addressing these issues, this paper proposes a method to acquire the global receptive field, termed G AN et, which extracts vein features using Gabor filters and computes an attention mechanism to capture the global receptive field for downstream palm vein recognition models. Initially, vein features are extracted using multi-scale fixed Gabor filters and multi-scale adaptive Gabor filters. Subsequently, self-attention mechanisms are employed to compute relationships between blocks to obtain the global receptive field. To perform recognition, the Euclidean distance between feature vectors is then computed. Our experiments on three datasets show that our approach outperforms existing palm vein recognition methods.