Human Ear Recognition Based on Gabor Wavelet and Supervised Locality Preserving Projection

A new ear recognition algorithm based on Gabor wavelet and supervised locality preserving projection (SLPP) is presented in this paper to solve the difficult problem of ear recognition with ear pose variation. Considering the redundancy in the high dimensional Gabor feature vectors, ear images are first described sparsely by statistical edge points sampling. Then a criterion with discriminating power is employed to evaluate the classification ability of the Gabor coefficients. The Gabor coefficients most favorable to the recognition are selected to construct new Gabor features. Our experiment results on ear database show that the proposed method produces less number of Gabor features and achieves high recognition rate with supervised locality preserving projection. The method is also robust to the ear pose variation.