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.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Ying Shen,et al.  Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach , 2017, Pattern Recognit..

[3]  Jing Wang,et al.  A Finger Vein Image-Based Personal Identification System With Self-Adaptive Illuminance Control , 2017, IEEE Transactions on Instrumentation and Measurement.

[4]  Liu Chongji.  High-Resolution Palmprint Minutiae Extraction Based on Gabor Phase and Image Quality Estimation , 2015 .

[5]  Zhenhua Guo,et al.  Extracting region of interest for palmprint by convolutional neural networks , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[6]  Liu Dian,et al.  Contactless palmprint recognition based on convolutional neural network , 2016, 2016 IEEE 13th International Conference on Signal Processing (ICSP).

[7]  Vincent Lepetit,et al.  A fast local descriptor for dense matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Yan Zhu,et al.  Lattice-based key exchange on small integer solution problem , 2014, Science China Information Sciences.

[9]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[10]  David Zhang,et al.  A survey of palmprint recognition , 2009, Pattern Recognit..

[11]  Yongjie Chu,et al.  Low-resolution face recognition with single sample per person , 2017, Signal Process..

[12]  J. Dipierri,et al.  Study of fingerprints in Argentina population for application in personal identification. , 2017, Science & justice : journal of the Forensic Science Society.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Fabrizio Smeraldi,et al.  Personal identification based on skin texture features from the forearm and multi‐modal imaging , 2017, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[17]  Qiang Zhang,et al.  Supervised Hashing with Deep Convolutional Features for Palmprint Recognition , 2017, CCBR.

[18]  Y. Wang,et al.  Dual-tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition , 2009, Comput. Informatics.

[19]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[20]  Pan Xin,et al.  Palmprint recognition based on deep learning , 2015 .

[21]  Jufu Feng,et al.  High-resolution palmprint minutiae extraction based on Gabor feature , 2014, Science China Information Sciences.

[22]  Tieniu Tan,et al.  A Code-Level Approach to Heterogeneous Iris Recognition , 2017, IEEE Transactions on Information Forensics and Security.

[23]  Xin Yang,et al.  Local Difference Binary for Ultrafast and Distinctive Feature Description , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Wei Jia,et al.  Palmprint Recognition Based on Complete Direction Representation , 2017, IEEE Transactions on Image Processing.