Face recognition across pose: A review
Pattern Recognition
Pose Normalization for Local Appearance-Based Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Pose manifold curvature is typically less near frontal face views
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Towards pose-invariant 2D face classification for surveillance
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Gamer's facial cloning for online interactive games
International Journal of Computer Games Technology - Special issue on cyber games and interactive entertainment
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Overlapping local phase feature (OLPF) for robust face recognition in surveillance
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Pose-invariant face recognition in videos for human-machine interaction
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Robust frontal view search using extended manifold learning
Journal of Visual Communication and Image Representation
Automated diagnosis of otitis media: vocabulary and grammar
Journal of Biomedical Imaging - Special issue on Computer Vision and Image Processing for Computer-Aided Diagnosis
Automated diagnosis of otitis media: vocabulary and grammar
Journal of Biomedical Imaging
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Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.