Ear Recognition Based on the SIFT Descriptor with Global Context and the Projective Invariants

A novel ear recognition approach is proposed in this paper, which use the SIFT descriptor with global context and the projective invariants to obtain ear features. At first, as the ear images have multiple similar local regions, the SIFT descriptor with global context is used for computing the matching points. This kind of descriptor can discriminate the keypoints with similar local appearances effectively. Then the number of the matching points is used for recognition. Finally, five projective invariants are obtained by computing the cross ratios of five collinear points on the longest axis. Both the number of the matching points and the projective invariants are used for constructing recognition feature. The nearest neighbor method is used for classification. Extensive experiments have performed to valid its efficiency.