Variable Curvature Gabor Convolution and Multi-Branch Structures for Finger Vein Recognition

Gabor filters are able to extract texture features from finger vein images from different directions and scales. However, manually crafted Gabor filters have problems such as relatively single direction and scale, and difficulties in parameter adjustment to adapt to specific datasets. To solve these problems, this paper proposes a neural network with a learnable variable curvature Gabor (VC-Gabor) convolutional layer. Firstly, the Gabor filter is improved by adding variable curvature to extract information about different curvature degrees in the vein curves. Secondly, the VC-Gabor filter is designed as a learnable convolutional filter, with parameters updated using neural network back-propagation. This facilitates the enrichment of learned VC-Gabor filter directions, scales, and curvatures, eliminating the need for intricate manual parameter tuning. Finally, we propose adaptive multi-branch structures for feature extraction, which are used to enhance the feature extraction capability of the model. Experimental results on publicly available datasets FV-USM and SDUMLA demonstrate that the proposed algorithm improves recognition accuracy and reduces the Equal Error Rate (EER), thereby substantiating the effectiveness of the approach.