3D face recognition via conformal representation

In this paper, we propose a 3D face recognition approach based on the conformal representation of facial surfaces. Firstly, facial surfaces are mapped onto the 2D unit disk by Riemann mapping. Their conformal representation (i.e. the pair of mean curvature (MC) and conformal factor (CF) ) are then computed and encoded to Mean Curvature Images (MCIs) and Conformal Factor Images (CFIs). Considering that different regions of face deform unequally due to expression variation, MCIs and CFIs are divided into five parts. LDA is applied to each part to obtain the feature vector. At last, five parts are fused on the distance level for recognition. Extensive experiments carried out on the BU-3DFE database demonstrate the effectiveness of the proposed approach.

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