Disentangled Representation and Enhancement Network for Vein Recognition

Abstract

Recently, vein recognition has been paid more considerable attention in biometric recognition fields. In the process of vein image acquisition, due to the influence of external factors such as illumination change, the texture information of vein images with the same identity information may change, which enhances the difference of intra-class and extremely degrades the performance of vein recognition systems. To address this problem, we proposed a Disentangled Representation and Enhancement Network for vein recognition called DRE-Net. First, robust vein shape masks are obtained by the designed vein segmentation algorithms, which are utilized as label information in DRE-Net to acquire the shape features of vein images. Second, a disentangled representation network which contains two encoders and two decoders is designed to disentangle the texture features and shape features of vein images. Besides, a Multi-Scale Attention Residual Block (MSARB) is presented to better mine the vein network information and enhance the representation ability of DRE-Net for vein images. Finally, a Weight-Guided Feature Enhancement Module (WGFEM) is presented to obtain more discriminative representations for vein recognition by reducing the importance of texture features and increasing the importance of shape features, which decreases the difference of intra-class caused by illumination change. Extensive experiments have been carried out on three benchmark vein databases, and the experimental results demonstrate that our proposed model outperforms the state-of-the-art methods.