Ear Recognition Based on Gabor Features and KFDA

The Scientific World Journal
Volume 2014 (2014), Article ID 702076, 12 pages
Published 17 March 2014
http://dx.doi.org/10.1155/2014/702076

Li Yuan [1,2] and Zhichun Mu [1]

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

Abstract

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.

Introduction

The research on ear recognition has been drawing more and more attention in recent five years [1–4]. Based on the research of the “Iannarelli system” [5], the structure of the ear is fairly stable and robust to changes in facial expressions or aging. Ear biometrics is noncontacting and so it can be applied for human identification at a distance, making it a helpful supplement to facial recognition. An ear recognition system based on 2D images is composed of the following stages: ear enrollment, feature extraction, and ear recognition/authentication. The stage of ear enrollment includes automatic ear detection and ear normalization.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant no. 61300075 and Fundamental Research Funds for China Central Universities under Grant no. FRF-SD-12-017A.

http://www.hindawi.com/journals/tswj/2014/702076/