Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
Proceedings of the 50th Annual Design Automation Conference
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In this paper, we propose a novel method of illumination normalization developed on the basis of the retinex theory. In retinex based methods, illumination is generally estimated and normalized by first smoothing the input image and then dividing the estimate into the original input image. The proposed method estimates illumination by iteratively convolving the input image with a 3×3 averaging mask weighted by an efficient measure of the illumination discontinuity at each pixel. In this way, we could achieve a fast illumination normalization in which even face images with strong shadows are normalized efficiently. The proposed method has been evaluated based on the Yale face database B and the CMU PIE database by using PCA. Carrying out various scenarios of test, we have found that our method presented consistent and promising results even when we used images with the worst case of illumination as training sets. We believe that the proposed method has a great potential to be applied to real time face recognition systems, especially under harsh illumination conditions.