Gender classification using 2-D ear images and sparse representation

Gender classification attracted the attention of researchers in computer vision for its use in many applications. Researches have addressed this issue based on facial images. In this paper, we present the first approach for gender classification using 2-D ear images based upon sparse representation. In sparse representation, the training data is used to develop a dictionary based on extracted features. In this work, Gabor filters are used for feature extraction. Classification is achieved by representing the test data using the dictionary based upon the extracted features. Experimental results conducted on the University of Notre Dame (UND) collection J dataset, containing large appearance, pose, and lighting variability, yielded gender classification rate of 89.49%.