Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigen-Texture Method: Appearance Compression and Synthesis Based on a 3D Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Journal of Cognitive Neuroscience
Object recognition based on photometric alignment using ransac
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape
Image and Vision Computing
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This paper presents a robust face recognition method which can work even when an insufficient number of images are registered for each person. The method is composed of image correction and image decomposition, both of which are specified in the normalized image space (NIS). The image correction[1, 2] is realized by iterative projections of an image to an eigenspace in NIS. It works well for natural images having various kinds of noise, including shadows, reflections, and occlusions. We have proposed decomposition of an eigenface into two orthogonal eigenspaces[3], and have shown that the decomposition is effective for realizing robust face recognition under various lighting conditions. This paper shows that the decomposed eigenface method can be refined by projection-based image correction.