Human Ear Image Recognition Method Using PCA and Fisherface Complementary Double Feature Extraction

Abstract

Ear recognition is a new kind of biometric identification technology now. Feature extraction is a key step in pattern recognition technology, which determines the accuracy of classification results. The method of single feature extraction can achieve high recognition rate under certain conditions, but the use of double feature extraction can overcome the limitation of single feature extraction. In order to improve the accuracy of classification results, this paper proposes a new method, that is, the method of complementary double feature extraction based on PCA and Fisherface, and we apply it to human ear image recognition. Experimental results on the ear image database provided by Beijing University of Science and Technology show that the ear recognition rate of the proposed method is significantly higher than the single feature extraction using PCA, Fisherface or ICA alone.