Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
2D and 3D face recognition: A survey
Pattern Recognition Letters
Bio security using face recognition for industrial use
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Perfect histogram matching PCA for face recognition
Multidimensional Systems and Signal Processing
Fovea intensity comparison code for person identification and verification
Engineering Applications of Artificial Intelligence
Similarity rank correlation for face recognition under unenrolled pose
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
3D human face soft tissues landmarking method: An advanced approach
Computers in Industry
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We present a framework for pose-invariant face recognition using parametric linear subspace models as stored representations of known individuals. Each model can be fit to an input, resulting in faces of known people whose head pose is aligned to the input face. The model's continuous nature enables the pose alignment to be very accurate, improving recognition performance, while its generalization to unknown poses enables the models to be compact. Recognition systems with two types of parametric linear model are compared using a database of 20 persons. The results showed our system's robust recognition of faces with plus-minus 50 degree range of full 3D head rotation, while compressing the data by a factor of 20 and more.