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.
Highlights
The research on ear recognition has been drawing more and more attention in recent five years [1,2,3,4]
Mu et al (2004) proposed a long axis based shape and structural feature extraction method; the shape feature consisted of the curve fitting parameters of the outer ear contour, the structural feature was composed of ratios of the length of key sections to the length of the long axis, and nearest neighborhood classifier was used for recognition
An ear recognition system based on 2D images is proposed
Summary
The research on ear recognition has been drawing more and more attention in recent five years [1,2,3,4]. An ear recognition system based on 2D images is composed of the following stages: ear enrollment, feature extraction, and ear recognition/authentication. The ear images used in different methods were not normalized based on the same standard. Mu et al (2004) proposed a long axis based shape and structural feature extraction method; the shape feature consisted of the curve fitting parameters of the outer ear contour, the structural feature was composed of ratios of the length of key sections to the length of the long axis, and nearest neighborhood classifier was used for recognition. Dun and Mu (2009) proposed an ICA based ear recognition method through nonlinear adaptive feature fusion. Many appearance based methods will not work in this situation This means that there exists a gap between ear detection and feature extraction.
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