Face recognition across pose: A review
Pattern Recognition
Pose-Invariant Face Matching Using MRF Energy Minimization Framework
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Color face recognition for degraded face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Biometric classifier update using online learning: A case study in near infrared face verification
Image and Vision Computing
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Plastic surgery: a new dimension to face recognition
IEEE Transactions on Information Forensics and Security
Computer Vision and Image Understanding
Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition
International Journal of Computer Vision and Image Processing
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Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiproflle face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.