A Novel Finger Vein Verification Framework Based on Siamese Network and Gabor Residual Block

Qiong Yao, Chen Chen, Dan Song, Xiang Xu () and Wensheng Li
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Qiong Yao: Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Chen Chen: Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Dan Song: Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Xiang Xu: Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Wensheng Li: Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China

Mathematics, 2023, vol. 11, issue 14, 1-26

Abstract: The evolution of deep learning has promoted the performance of finger vein verification systems, but also brings some new issues to be resolved, including high computational burden, massive training sample demand, as well as the adaptability and generalization to various image acquisition equipment, etc. In this paper, we propose a novel and lightweight network architecture for finger vein verification, which was constructed based on a Siamese framework and embedded with a pair of eight-layer tiny ResNets as the backbone branch network. Therefore, it can maintain good verification accuracy under the circumstance of a small-scale training set. Moreover, to further reduce the number of parameters, Gabor orientation filters (GoFs ) were introduced to modulate the conventional convolutional kernels, so that fewer convolutional kernels were required in the subsequent Gabor modulation, and multi-scale and orientation-insensitive kernels can be obtained simultaneously. The proposed Siamese network framework (Siamese Gabor residual network (SGRN)) embeds two parameter-sharing Gabor residual subnetworks (GRNs) for contrastive learning; the inputs are paired image samples (a reference image with a positive/negative image), and the outputs are the probabilities for accepting or rejecting. The subject-independent experiments were performed on two benchmark finger vein datasets, and the experimental results revealed that the proposed SGRN model can enhance inter-class discrepancy and intra-class similarity. Compared with some existing deep network models that have been applied to finger vein verification, our proposed SGRN achieved an ACC of 99.74 % and an EER of 0.50 % on the FV-USM dataset and an ACC of 99.55 % and an EER of 0.52 % on the MMCBNU_6000 dataset. In addition, the SGRN has smaller model parameters with only 0.21 × 10 6 Params and 1.92 × 10 6 FLOPs, outperforming some state-of-the-art FV verification models; therefore, it better facilitates the application of real-time finger vein verification.

Keywords: finger vein verification; Siamese network; Gabor residual block; contrastive learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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