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In this work, we propose and experiment an original solution to 3D face recognition which supports partial matching of facial scans as occurs in the case of missing parts and occlusions. In the proposed approach, distinguishing traits of the face are captured by first extracting SIFT keypoints on the face scan and then measuring how the face changes along facial curves defined between pairs of keypoints. Facial curves are also associated with a measure of salience so as to distinguish curves that model characterizing traits of some subjects from curves that are frequently observed in the face of many different subjects. The recognition accuracy of the approach has been experimented on the Face Recognition Grand Challenge dataset.

Partial match of 3D faces using facial curves between SIFT keypoints