Complementary Double Feature Extraction of the Human Ear Image Recognition Based on PCA and Fisherface

Ear recognition is a new biometric technique now.Feature extraction is the key step in pattern recognition technology, determines the classification results of high accuracy or low accuracy.Single feature extraction can get high recognition rate under certain conditions, but adopt double feature extraction can overcome single feature extraction of this limitation.In order to improve the recognition rate of classified results, this paper proposes a new method-complementary double feature extraction method which is based on principal component analysis (PCA) and fisherface.And this method is applied to the images recognition of human ear.The experimental results from the human ear image library which is provided by Beijing University of Science and Technology shows that the human ear recognition rate of this method is obviously higher than the one of single feature extraction of PCA 、fisherface and ICA.