Three-dimensional face recognition using variance-based registration and subject-specific descriptors

Abstract

Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.