Static and dynamic 3D facial expression recognition: A comprehensive survey
Image and Vision Computing
Real-time expression recognition from dynamic sequences of 3D facial scans
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Geometry-based 3D face morphology analysis: soft-tissue landmark formalization
Multimedia Tools and Applications
Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans
The Visual Computer: International Journal of Computer Graphics
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Methods to recognize humans’ facial expressions have been proposed mainly focusing on 2D still images and videos. In this paper, the problem of person-independent facial expression recognition is addressed using the 3D geometry information extracted from the 3D shape of the face. To this end, a completely automatic approach is proposed that relies on identifying a set of facial keypoints, computing SIFT feature descriptors of depth images of the face around sample points defined starting from the facial keypoints, and selecting the subset of features with maximum relevance. Training a Support Vector Machine (SVM) for each facial expression to be recognized, and combining them to form a multi-class classifier, an average recognition rate of 78.43% on the BU-3DFE database has been obtained. Comparison with competitor approaches using a common experimental setting on the BU-3DFE database shows that our solution is capable of obtaining state of the art results. The same 3D face representation framework and testing database have been also used to perform 3D facial expression retrieval (i.e., retrieve 3D scans with the same facial expression as shown by a target subject), with results proving the viability of the proposed solution.