Research on Finger Vein Recognition Algorithm Based on Wavelet‐Transformer

ABSTRACT Finger vein recognition, like control systems, requires harmonizing local and global dynamics for optimal performance. To address limitations in existing methods, we propose the wavelet‐transformer algorithm, combining CNNs for local feature extraction, vision transformers (ViT) for global dependency modeling, and discrete wavelet transforms (DWT) for time‐frequency analysis. This modular design mirrors control theory principles, ensuring stability and adaptability. Experiments on FV210 and FV618 datasets show the algorithm's superior performance, achieving recognition accuracies of 99.53% and 97.62%, with equal error rates of 0.35% and 0.71%, highlighting its robustness for intelligent recognition and control applications.