Human Ear recognition based on Multi-scale Local Binary Pattern descriptor and KL divergence

This paper presents a novel human ear recognition approach based on Multi-scale Local Binary Pattern (MLBP) descriptor to enhance the recognition performance. The proposed method includes the following two steps: (i) the feature extraction step that computes the MLBP descriptor-based features from human ear images, and (ii) the matching process that uses the Kullback Leibler (KL) distance to capture efficiently the similarities/dissimilarities between the feature vectors and then make a decision. The proposed method is performed using the IIT Delhi Ear database and then compared to the state-of-the-art methods. The results obtained have shown that the proposed method achieves satisfying identification performances up to 95% in terms of rank-1 identification rate.