Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
The FERET Evaluation Methodology for Face-Recognition Algorithms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The FERET Verification Testing Protocol for Face Recognition Algorithms
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
Retrospective illumination correction of greyscale historical aerial photos
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Robust video authentication system over internet protocol
International Journal of Biometrics
A novel illumination normalization method for face recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
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Most of the FR (face recognition) systems suffer from sensitivity to variations in illumination. For better performance the FR system needs more training samples acquired under variable lightings but it is not practical in real world. We introduce a novel pre-processing method, which makes illumination-normalized face image for face recognition. The proposed method, ICR (Illumination Compensation based on Multiple Regression Model), is to find the plane that best fits the intensity distribution of the face image using the multiple regression model, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experiments show a significant improvement of the recognition rate.