FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling

In this paper, we present FVR-Net, which is a novel finger vein recognition network using a convolutional neural network (CNN) with a hybrid pooling mechanism. The scheme is based on the use of a block-wise feature extraction network to extract discrete features from interclass vein image samples, regardless of their visual quality. Input images to FVR-Net are subjected to preprocessing prior to being fed into the network in order to segment the vein patterns from the background. We designed a feature extraction network in which each block consists of a convolutional layer followed by hybrid pooling, whose output activation maps are concatenated before passing features to another block within the network. In the hybrid pooling layer, two subsampling layers of maxpooling and average pooling are placed in parallel where the former activates the most discrete features of the input, and the latter considers the complete extent of the input volume so better localization of features can be accessed. After the features are extracted, they are passed to three fully connected layers (FCLs) for classification. We conduct several experiments on two publicly available finger vein datasets based on visual quality of the images. When compared to conventional studies, the proposed model achieves outstanding recognition performance with accuracies of up to 97.84% and 97.22% for good and poor-quality images, respectively. By varying multiple network hyperparameters, we obtain optimal settings such that the model can guarantee the best recognition accuracy for a finger vein biometric system.