LAUSR

Fingerprint recognition technology is widely used as a kind of powerful and effective authentication method on various mobile devices. However, most mobile devices use small-area fingerprint scanners, and these fingerprint scanners can only obtain a part of the user’s fingerprint information. Besides, traditional fingerprint recognition algorithms excessively rely on the details of fingerprints, and their recognition performance has great limitations in mobile devices which can only get partial fingerprint images due to fingerprint scanners. This paper proposes a partial fingerprint recognition algorithm based on deep learning for the recognition of partial fingerprint images. It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction and finally improve the recognition performance of partial fingerprint images. The experimental results show that the fingerprint recognition algorithm in this paper has a better performance than the existing fingerprint recognition algorithm based on deep learning on the problem of partial fingerprint classification and fingerprint recognition in the public dataset NIST-DB4 and self-built dataset NCUT-FR.