Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
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
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
Journal of Cognitive Neuroscience
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Object recognition based on photometric alignment using ransac
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fractal Model Based Face Recognition for Ubiquitous Environments
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Gaussian decomposition for robust face recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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The present paper discusses a method for robust face recognition that works even when only one image is registered and the test image contains a lot of local noises. Two types of facial image decomposition are compared both theoretically and experimentally. That is, we consider both a projectional decomposition, in which images are decomposed into individuality and other components, and a locational decomposition, in which the effects of local noises are suppressed. These two decompositions are simple and powerful and can be applied in collaboration with one another. This collaboration can be realized in a straightforward manner because the decompositions are consistent with one another. They work in a complementary manner and provide better results than when the decompositions are used independently. Finally, we report experimental results obtained using three databases. These results indicate that the combination of projectional and locational decompositions works well, even when only one image is registered and the test images contain significant noise.