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.