Using 2D wavelet and principal component analysis for personal identification based On 2D ear structure

Ear structure as a new class of biometrics can be used in many applications such as security systems. Ear structure is physiologically unique and stable, so ear recognition can be a good choice for a biometric security system. Even though, using ear biometric is not customary but it can be used with other biometrics like face or fingerprint simultaneously to increase the reliability of a biometric security system. In this paper, regarding existence of important information in edges and high frequency points in ear structure, we apply 2D wavelet to the normalized image. By decomposing the image into three images (horizontal, vertical and diagonal) using wavelet, we find three independent features in three directions. We combine these decomposed images to reach the feature matrix. This allows considering the changes in the ear images from three basic directions simultaneously. We apply PCA on feature matrix to feature dimension reduction and classification. Our experience in using this approach for different images demonstrates the accuracy of 90.5% and 95.05% recognition rate for two sets of databases.