关键词: 指纹识别, 密集连接卷积网络, 二值特征, 深度学习

Abstract:

Focused on the issue that the accuracy rate of the traditional fingerprint methods based on minutiae reduces significantly when dealing with the small-size fingerprint, a small-size fingerprint matching method based on densely connected convolutional networks is presented. Firstly, the image enhancement of original fingerprint images is performed. Additional, a deep learning model for extracting fingerprint features is built with the advantages of the feature reuse of densely connected convolutional networks, and the binary features is introduced. Finally, the small-size fingerprint registration and recognition are accomplished according to the binary features and feature vectors of fingerprint images. The experimental results show that the proposed method achieves a correct recognition rate of 98.57% in the self-built database, which is much higher than the traditional fingerprint matching methods based on minutiae and meets the application requirements of the intelligent mobile terminal.

Key words: fingerprint recognition, densely connected convolution network, binary features, deep learning