Palmprint Recognition Using Siamese Network
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.
KeywordsPalmprint recognition Siamese network Convolutional Neural Networks Feature extraction
This work is supported by grants from National Natural Science Foundation of China (No. 61105021), Natural Science Foundation of Shaanxi, China (No. 2015JQ6257) and the Fundamental Research Funds for the Central Universities.
- 1.Liu, D., Sun, D.M.: Contactless palmprint recognition based on convolutional neural network. In: Baozong, Y., Qiuqi, R., Yao, Z., Gaoyun, A.N. (eds.) Proceedings of 2016 IEEE 13th International Conference on Signal Processing, pp. 1363–1367. IEEE, New York (2016)Google Scholar
- 10.Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE, New York (2015)Google Scholar
- 11.Liu, C., Wang, H., Feng, J.: High-resolution palmprint minutiae extraction based on gabor phase and image quality estimation. Acta Sci. Nat. Univ. Pekin. (China) 51, 384–390 (2015)Google Scholar
- 12.Feng, J.F., Liu, C.J., Wang, H., Sun, B.: High-resolution palmprint minutiae extraction based on Gabor feature. Sci. China-Inf. Sci. 57, 1–15 (2014)Google Scholar
- 13.Wang, H., Liu, C.-J., Fu, X., Feng, J.-F.: Quality estimation algorithm based on learning for high-resolution palmprint minutiae. J. Softw. (China) 25, 2180–2186 (2014)Google Scholar
- 17.Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE, New York (2014)Google Scholar
- 19.Zhao Dandan Pan, X., Pan, X., Luo, X., Gao, X.: Palmprint recognition based on deep learning. In: Proceedings of 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015), pp. 214–216 (2015)Google Scholar
- 20.Bao, X.J., Guo, Z.H.: Extracting region of interest for palmprint by convolutional neural networks. In: Lopez, M.B., Hadid, A., Pietikainen, M. (eds.) Proceedings of 2016 Sixth International Conference on Image Processing Theory, Tools and Applications, pp. 1–6. IEEE, New York (2016)Google Scholar
- 21.Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching. In: Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)Google Scholar
- 24.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. 15, 1–14 (2014)Google Scholar