Adaptive Gabor Convolutional Neural Networks for Finger-Vein Recognition

Finger-vein recognition has become an important branch in the field of biometrics. Convolutional neural networks (CNNs) have achieved remarkable success in finger-vein recognition, but their models have many problems, such as high complexity, large parameters, running time slowly and so on. To solve these problems, we proposed adaptive Gabor convolutional neural networks (AGCNN). We replaced the normal convolution layer of CNNs with Gabor convolution layer, and systematically investigate comparative performance using AGCNN and CNNs in the raw image and region of interest (ROI) image of finger vein database respectively. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with slight performance decrease. And the experimental results of the raw image database show that the processing speed has been greatly improved.