Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves

Abstract This study proposes a 3D face recognition method using multiple subject-specific curves insensitive to intra-subject distortions caused by expression variations. Considering that most sharp variances in facial convex regions are closely related to the bone structure, the convex crest curves are first extracted as the most vital subject-specific facial curves based on the principal curvature extrema in convex local surfaces. Then, the central profile curve and the horizontal contour curve passing through the nose tip are detected by using the precise localization of the nose tip and symmetry plane. Based on their discriminative power and robustness to expression changes, the three types of curves are fused with appropriate weights at the feature-level and used for matching 3D faces with the iterative closest point algorithm. The combination of multiple expression-insensitive curves is complementary and provides sufficient and stable facial surface features for face recognition. In addition, for each convex crest curve, an expression-irrelevant factor is assigned as the adaptive weight to improve the face matching performance. The results of experiments using two public 3D databases, GavabDB and BU-3DFE, demonstrate the effectiveness of the proposed method, and its recognition rates on both databases reflect an encouraging performance.