AGCNN: Adaptive Gabor Convolutional Neural Networks with Receptive Fields for Vein Biometric Recognition

In recent years, finger vein recognition has attracted more attention and research as a secure method of identification. Convolutional neural networks have achieved great success in the field of finger vein recognition, yet they suffer from high computational complexity, large parameters, and other challenges. To solve these problems, we propose a Gabor convolutional neural network with receptive fields. We use Gabor filters with receptive field properties to design Gabor convolutional layers. Then we replace the conventional convolutional layer with the Gabor convolutional layer; analyze the influence of different loss functions, convolution kernel size, and feature size on the network model; and choose the most suitable model parameters and loss function. Finally, we systematically investigate comparative performance using AGCNN and CNNs in different finger vein databases. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with a slight performance decrease.