Abstract
Ear biometrics attracted the attention of researchers in computer vision and machine learning for its use in many applications. In this paper, we present a fully automated system for recognition from ear images based upon sparse representation. In sparse representation, extracted features from the training data is used to develop a dictionary. Classification is achieved by representing the extracted features of the test data as a linear combination of entries in the dictionary. In fact, there are many solutions for this problem and the goal is to find the sparsest solution. We use a relatively new algorithm named smoothed l super(0) norm to find the sparsest solution and Gabor wavelet features arc used for building the dictionary. Furthermore, we expand the proposed approach for gender classification from ear images. Several researches have addressed this issue based on facial images. We introduce a novel approach based on majority voting for gender classification. Experimental results conducted on the University of Notre Dame (UND) collection J data set, containing large appearance, pose, and lighting variations, resulted in a gender classification rate of 89.49%. Furthermore, the proposed method is evaluated on the WVU data set and classification rates for different view angles are presented. Results show improvement and great robustness in gender classification over existing methods.
