Palmprint recognition using siamese network

Abstract

Recently, palmprint representation using different descriptors under the incorporation of deep neural networks, always achieves significant recognition performance. In this paper, we proposed a novel method to achieve end-to-end palmprint recognition by using Siamese network. In our network, two parameter-sharing VGG-16 networks were employed to extract two input palmprint images’ convolutional features, and the top network directly obtained the similarity of two input palmprints according to their convolutional features. This method had a good performance on PolyU dataset and achieved a high recognition outcome with an Equal Error Rate (EER) of 0.2819%. To test the robustness of the proposed algorithm, we collected a palmprint dataset called XJTU from the practical daily environment. On XJTU, the EER of our method is 4.559%, which highlighted a promising potential of the usage of palmprint in personal identification system.