Pose-invariant 3D face recognition using half face

Pose variations are still challenging problems in 3D face recognition because large pose variations will cause self-occlusion and result in missing data. In this paper, a new method for pose-invariant 3D face recognition is proposed to handle significant pose variations. For pose estimation and registration, a coarse-to-fine strategy is proposed to detect landmarks under large yaw variations. At the coarse search step, candidate landmarks are detected using HK curvature analysis and subdivided according to a facial geometrical structure-based classification strategy. At the fine search step, candidate landmarks are identified and labeled by comparing with a Facial Landmark Model. By using the half face matching, we perform the matching step with respect to frontal scans and side scans. Experiments carried out on the Bosphorus and UND/FRGC v2.0 databases show that our method has high accuracy and robustness to pose variations.