LOCAL DESCRIPTORS MATCHING FOR 3D FACE RECOGNITION

An original solution to 3D face recognition, which supports face matching also in the case of probes with varying expressions and missing parts is proposed in this work. Distinguishing traits of the face are captured by first extracting 3D keypoints of the face scan, then measuring how the face surface changes in the neighborhood of the keypoints using a local descriptor. To this end, an adaptation of the meshDOG detector to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed on the Bosphorus database, showing competitive results with respect to existing solutions for 3D face biometrics.