Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization

Abstract

Finger vein recognition is one of a new biometric recognition technology, which has a wide range of applications in daily life. However, the quality of finger vein images is less than satisfactory because of the disappointing sensor conditions based on infrared light. This problem results in the inaccuracy of finger rein identification. In order to solve this problem and speed up convergence, this paper introduces a new approach of identifying finger veins using the Convolutional Neural Networks (CNNs) with center loss function and dynamic regularization. The proposed method will makes full use of the labels and then ameliorate results. We compare its performance with several popular loss function, such as softmax loss and triplet loss. Experiments were carried out on the datasets of MMCBNU_6000 and FV-USM, whose results show that not only the proposed loss function minimize the error rate and is less time-consuming, but also avoid overfitting.