IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
A Hybrid Technique for Facial Feature Point Detection
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A comparison of subspace analysis for face recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Journal of Cognitive Neuroscience
Face alignment under variable illumination
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Eigen-harmonics faces: face recognition under generic lighting
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Generally, the performance of present day computer vision systems is still very much affected by varying brightness and light source conditions. Recently, Koenderink suggested that this weakness is due to methodical flaws in low level image processing. As a remedy, he develops a new theory of image modeling. This paper reports on applying his ideas to the problem of illumination insensitive face detection. Experimental results will underline that even a simple and conventional method like principal component analysis can accomplish robust and reliable face detection in the presence of illumination variation if applied to curvature features computed in Koenderink's image space.