Attention Label Learning to Enhance Interactive Vein Transformer for Palm-Vein Recognition

Abstract

In recent years, vein biometrics has gained significant attention due to its high security and privacy features. While deep neural networks have become the predominant classification approaches for their ability to automatically extract discriminative vein features, they still face certain drawbacks: 1) Existing transformer-based vein classifiers struggle to capture interactive information among different attention modules, limiting their feature representation capacity; 2) Current label enhancement methods, although effective in learning label distributions for classifier training, fail to model long-range relations between classes. To address these issues, we present ALE-IVT, an Attention Label Enhancement-based Interactive Vein Transformer for palm-vein recognition. First, to extract vein features, we propose an interactive vein transformer (IVT) consisting of three branches, namely spatial attention, channel attention, and convolutional module. In order to enhance performance, we integrate an interactive module that facilitates the sharing of discriminative features among the three branches. Second, we explore an attention-based label enhancement (ALE) approach to learn label distribution. ALE employs a self-attention mechanism to capture correlation between classes, enabling the inference of label distribution for classifier training. As self-attention can model long-range dependencies between classes, the resulting label distribution provides enhanced supervised information for training the vein classifier. Finally, we combine ALE with IVT to create ALE-IVT, trained in an end-to-end manner to boost the recognition accuracy of the IVT classifier. Our experiments on three public datasets demonstrate that our IVT model surpasses existing state-of-the-art vein classifiers. In addition, ALE outperforms current label enhancement approaches in term of recognition accuracy.