Ear Recognition using a novel Feature Extraction Approach
Most of traditional ear recognition methods that based on local features always need accurate images alignment, which may severely affect the performance. In this paper, we investigate a novel approach for ear recognition based on Polar Sine Transform (PST); PST is free of images alignment. First, we divide the ear images into overlapping blocks. After that, we compute PST coefficients that are employed to extract invariant features for each block. Second, we accumulate these features for only one feature vector to represent ear image. Third, we use Support Vector Machine (SVM) for ear recognition. To validate the proposed approach, experiments are performed on USTB database and results show that our approach is superior to previous works.
Keywords: Ear recognition; Feature extraction; PST; SVM.
ABOUT THE AUTHORS
Ibrahim Omara received the Bachelorís degree in Mathematics and Computer Science from Faculty of Science, Menoufia University, Egypt during the period of September 2001 to July 2005, and he received his Master degree on computer science from the same University in 2012. From April 2007 to September 2014, he was an assistant lecture in the Faculty of Science, Menoufia University, Egypt. Currently, he is pursuing the Ph.D. degree with the Department of Computer Science at the School of Computer Science and Technology, Harbin Institute of Technology (HIT), China. His current research interests include Computer vision, Biometrics, Multi-biometrics and Machine learning.
Feng Li is a Ph.D. student in School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. His current research interests include metric learning and image classification.
Ahmed Hagag received the B.Sc. (Honors) in pure mathematics and computer science from the Faculty of Science, Menoufia University, Egypt, in 2008, and he received his M.Sc. in computer science from the same university in 2013. He joined the teaching staff of the Faculty of Computer and Information Technology, Egyptian E-Learning University, Cairo, Egypt, in 2009. Currently, he is pursuing the Ph.D. degree with the Department of Computer Science at the School of Computer Science and Technology, Harbin Institute of Technology (HIT), China. His research interests are compression, classification, de-nosing, and wireless communication for satellite multispectral and hyperspectral images.
Souleyman Chaib was born in Mostaganem, Algeria, in 1988. Received the B.S. and M.S. degrees in computer science from the University of sciences and technology of Oran - Mohamed Boudiaf, Algeria, in 2009 and in 2011, respectively. He is currently working toward the Ph.D. degree with the School of computer science and Technology, Harbin Institute of Technology. His research interests include very high resolution image classification and scene classification.
Wangmeng Zuo received the Ph.D. degree in computer application technology from the Harbin Institute of Technology, Harbin, China, in 2007. From July 2004 to December 2004, from November 2005 to August 2006, and from July 2007 to February 2008, he was a Research Assistant at the Department of Computing, Hong Kong Polytechnic University, Hong Kong. From August 2009 to February 2010, he was a Visiting Professor in Microsoft Research Asia. He is currently an Associate Professor in the School of Computer Science and Technology, Harbin Institute of Technology. His current research interests include discriminative learning, image modeling, low level vision, and biometrics. Dr. Zuo has published more than 50 papers in top tier academic journals and conferences including IEEE T-IP, T-NNLS, T-IFS, CVPR, ICCV, ECCV, and NIPS. Dr. Zuo is an Associate Editor of the IET Biometrics, the Guest Editor of Neurocompu-ting and Pattern Recognition.