ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Illumination normalization with time-dependent intrinsic images for video surveillance
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Shadows produce troublesome effects in many computer vision applications. The idea behind most current shadow removal approaches is locating shadows and then removing them[1][4]. However, distinguishing shadow edges due to shadows from reflectance edges due to reflectance changes is a difficult problem, particularly in a single image. In this paper, we focus on the shadow removal problem in face recognition, and take a novel method based on ICA (Independent Component Analysis) to remove shadows from a single face images. The training set contains face images without shadows. Firstly, we applied derivative filters on training images to derive face edge maps, and then perform ICA on filtered training set to construct pixel ICA subspaces which can be used to remove shadow edges from the filtered versions of a single test image. After the shadow edges removal process, a shadow free image can be reconstructed using an approach similar to [7]. Unlike previous shadow removal approaches, our method can remove shadows from a single gray image. Experimental results demonstrate that the proposed approach can effectively eliminate the effects of shadows in face recognition.