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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Bauckhage, C., & Tsotsos, J. K. (2005). Image space I 3 and eigen curvature for illumination insensitive face detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3656 LNCS, pp. 456–463). https://doi.org/10.1007/11559573_57
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