Siamese Network Based on CNN for Fingerprint Recognition

Abstract

Fingerprint recognition is regarded as the most reliable and accurate biometric identification system. Most traditional fingerprint identification systems use digital image processing technology rather than new deep learning technology. While, provided that the image quality is bad, the accuracy of the traditional fingerprint system maybe could not reach the ideal value. In this paper, we created three different Siamese networks based on CNN structures to compare the impact of network structure on fingerprint recognition accuracy. The three CNN networks are self-defined net, AlexNet, and VGG. We use dynamic adjustment of the threshold to make it more adaptable to a variety of data sets. The biggest difference between these three networks is the number of parameters. In the experiment, we use the data set of 1000 factorial pairs, and the average accuracy of fingerprint recognition is 97%, 94%, and 51% respectively. And then, we mainly discuss that the Siamese network based on CNN has an excellent advantage in fingerprint recognition, and the struct of CNN is the key to affect the accuracy of fingerprint recognition.