Traditional biometric systems have encountered significant setbacks due to various unavoidable factors, for example, face recognition-based biometrics fails due to the wearing of face masks and fingerprints create hygiene concerns. This paper proposes a novel lightweight cross-spectral vision transformer (CS-ViT) for biometric authentication using forehead subcutaneous vein patterns and periocular patterns, offering a promising alternative to traditional methods, capable of performing well even with the face masks and without any physical touch. The proposed framework comprises a cross-spectral dual-channel architecture designed to handle two distinct biometric traits and to capture inter-dependencies in terms of relative spectral patterns. Each channel consists of a Phase-Only Correlation Cross-Spectral Attention (POC-CSA) that captures their individual as well as correlated patterns. The computation of cross-spectral attention using POC extracts the phase correlation in the spatial features. Therefore, it is robust against the resolution/intensity variations and illumination of the input images, assuming both biometric traits are from the same person. The lightweight model is suitable for edge device deployment. The performance of the proposed algorithm was rigorously evaluated using the Forehead Subcutaneous Vein Pattern and Periocular Biometric Pattern (FSVP-PBP) database. The results demonstrated the superiority of the algorithm over state-of-the-art methods, achieving a remarkable classification accuracy of 98.8% with the combined vein and periocular patterns.

翻译：传统生物特征识别系统因各种不可避免的因素遭遇了显著挫折，例如基于人脸识别的生物特征识别因佩戴口罩而失效，指纹识别则存在卫生隐患。本文提出了一种新颖的轻量级跨光谱视觉Transformer（CS-ViT），利用前额皮下静脉模式与眼周模式进行生物特征认证，为传统方法提供了具有前景的替代方案，能够在佩戴口罩且无需任何物理接触的情况下保持良好的性能。该框架包含一个跨光谱双通道架构，旨在处理两种不同的生物特征并捕获相对光谱模式间的相互依赖关系。每个通道包含一个相位相关跨光谱注意力模块（POC-CSA），用于捕获各自特征及其相关模式。利用POC计算跨光谱注意力可提取空间特征中的相位相关性，因此对输入图像的分辨率/强度变化及光照条件具有鲁棒性（假设两种生物特征来自同一人）。该轻量级模型适用于边缘设备部署。所提算法的性能使用前额皮下静脉模式与眼周生物特征模式（FSVP-PBP）数据库进行了严格评估。结果表明该算法优于现有先进方法，结合静脉与眼周模式实现了98.8%的卓越分类准确率。