A 3D ear recognition method based on auricle structural feature
The performances of most existing 3D ear recognition methods are degraded sharply by pose variation. In this paper, a 3D ear representation called 3D auricle structural feature(3DASF) and the corresponding pose robust 3D ear recognition method is presented. By measuring the surface characteristics through Surface Variation, 3DASF that contains ear key physiological structure is extracted. Then 3DASF corresponding points are used to implement iterative closest point(ICP) algorithm to coarse align gallery-probe ear pairs. Finally, fine alignment is performed to obtain the alignment errors for identity recognition. Experimental results conducted on University of Notre Dame(UND) biometric datasets collection F and collection G outperform the state-of-the-art 3D ear recognitions based on ICP. The results also demonstrate that the proposed method is more robust to pose variation than the state-of-the-art.
Keywords: 3D ear recognition, 3D feature extraction, auricle structure, iterative closest point.
Download Full-Text
ABOUT THE AUTHORS
Kai Wang
Kai Wang received the B.E. degree from the School of Electrical Engineering at Henan Polytechnic University in 2005. He is currently pursuing Ph.D. in Control Science and Control Engineering from the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interests include computer vision, pattern recognition, biometrics and machine learning.
Zhi-Chun Mu
Zhi-Chun Mu received the B.E. and M.E. degrees from the Department of Automation at University of Science and Technology Beijing in 1978 and 1983, respectively. He is currently a professor in the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He was a visiting scholar at Davy International UK and Sheffield Polytechnic, England, from 1989 to 1991, and a visiting Professor at University of Brighton, England from 1996 to 1999. As a Guest Professor, he visited Laboratoire d’Electronique, d’Informatique et d’Image, CNRS University of Burgundy France in 2007. He was the Chair of Organizing Committee of IEEE International Conference on Wavelet Analysis and Pattern Recognition 2007. He has served as a reviewer and member of Evaluation and Assessment Commission, Division of Information Science, National Natural Science Foundation of China since 2002.He is now Chair of IEEE SMC Beijing (capital region) Chapter. His main research interests include Pattern Recognition and Biometrics, Artificial Intelligence and its applications, Data Mining as well as Process Control and Modeling. He has published more than 180 refereed journal and conference papers in these areas. He is the corresponding author of this paper.
Kai Wang
Kai Wang received the B.E. degree from the School of Electrical Engineering at Henan Polytechnic University in 2005. He is currently pursuing Ph.D. in Control Science and Control Engineering from the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interests include computer vision, pattern recognition, biometrics and machine learning.
Zhi-Chun Mu
Zhi-Chun Mu received the B.E. and M.E. degrees from the Department of Automation at University of Science and Technology Beijing in 1978 and 1983, respectively. He is currently a professor in the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He was a visiting scholar at Davy International UK and Sheffield Polytechnic, England, from 1989 to 1991, and a visiting Professor at University of Brighton, England from 1996 to 1999. As a Guest Professor, he visited Laboratoire d’Electronique, d’Informatique et d’Image, CNRS University of Burgundy France in 2007. He was the Chair of Organizing Committee of IEEE International Conference on Wavelet Analysis and Pattern Recognition 2007. He has served as a reviewer and member of Evaluation and Assessment Commission, Division of Information Science, National Natural Science Foundation of China since 2002.He is now Chair of IEEE SMC Beijing (capital region) Chapter. His main research interests include Pattern Recognition and Biometrics, Artificial Intelligence and its applications, Data Mining as well as Process Control and Modeling. He has published more than 180 refereed journal and conference papers in these areas. He is the corresponding author of this paper.
