Facial curves between keypoints for recognition of 3D faces with missing parts
In this work, we propose and experiment an original solution to 3D face recognition that supports accurate face matching also in cases where just some parts of probe scans are available. In the proposed approach, distinguishing traits of the face are captured by first extracting keypoints of the 3D depth image and then measuring how the face depth changes along facial curves connecting pairs of key-points. Face similarity is evaluated by comparing facial curves across inlier pairs of keypoints that match between probe and gallery scans. In doing so, facial curves of the gallery scans are associated with a saliency measure in order 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 is experimented using the Face Recognition Grand Challenge v2.0 dataset.