Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A pose-wise linear illumination manifold model for face recognition using video
Computer Vision and Image Understanding
Local and global approximations for incomplete data
Transactions on rough sets VIII
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow.