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The Scientific World Journal
Volume 2014, Article ID 702076, 12 pages
Research Article

Ear Recognition Based on Gabor Features and KFDA

1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2Visualization and Intelligent Systems Laboratory, University of California Riverside, Riverside, CA, 92507, USA

Received 7 November 2013; Accepted 21 January 2014; Published 17 March 2014

Academic Editors: H. T. Chang and M. Nappi

Copyright © 2014 Li Yuan and Zhichun Mu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.