Experimental results demonstrated that the three principle curvature measures contain strong complementarity for 3D facial shape description, and their fusion can largely improve the recognition performance. The proposed method was evaluated on three public databases, i.e. Similarity comparison between faces is accomplished by matching all these curvature-based local shape descriptors using the sparse representation-based reconstruction method. To guarantee the pose invariance of these descriptors, three principle curvature vectors of these principle curvature measures are employed to assign the canonical directions. The local facial shape around each 3D feature point is comprehensively described by histograms of these principal curvature measures. Based on these estimated measures, the proposed method can automatically detect a set of sparse and discriminating 3D facial feature points. The strong theoretical basis of these measures provides us a solid discrete estimation method on real 3D face scans represented as triangle meshes. They can be reasonably computed based on the normal cycle theory and the geometric measure theory. These measures give a unified definition of principle curvatures for both smooth and discrete surfaces. This paper presents an effective 3D face keypoint detection, description and matching framework based on three principle curvature measures.
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