Human ear recognition based on SIFT feature
基于SIFT特征的人耳识别

The variety of ear angle and occlusion are the difficulties of ear recognition. The Scale Invariant Feature Transform (SIFT) is invariant to image scaling, translation and rotation. So the human ear recognition algorithm based on SIFT features was proposed. The SIFT features were computed from the ear image, and then image was divided into several overlapping grid regions, in which the local features of SIFT on each region are also computed. The matching similarity was computed between training image and test image, which was treated as global feature. The local feature and global feature were fused finally. The experiment results on ear database show that the algorithm works better than traditional global method, and is robust for the variety of ear angle and occlusion, and it is suitable for the recognition using the only one training sample.