Abstract
Pose-invariant face recognition (PIFR) refers to the ability that recognizes face images with arbitrary pose variations. Among existing PIFR algorithms, pose normalization has been proved to be an effective approach which preserves texture fidelity, but usually depends on precise 3D face models or at high computational cost. In this paper, we propose an highly efficient PIFR algorithm that effectively handles the main challenges caused by pose variation. First, a dense grid of 3D facial landmarks are projected to each 2D face image, which enables feature extraction in an pose adaptive manner. Second, for the local patch around each landmark, an optimal warp is estimated based on homography to correct texture deformation caused by pose variations. The reconstructed frontal-view patches are then utilized for face recognition with traditional face descriptors. The homography-based normalization is highly efficient and the synthesized frontal face images are of high quality. Finally, we propose an effective approach for occlusion detection, which enables face recognition with visible patches only. Therefore, the proposed algorithm effectively handles the main challenges in PIFR. Experimental results on four popular face databases demonstrate that the propose approach performs well on both constrained and unconstrained environments.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
