Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Robot Vision
Handbook of Face Recognition
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
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Personal identification by finger vein images based on tri-value template fuzzy matching
WSEAS Transactions on Computers
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Robust face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. However, there is no feature vector invariant under illumination changes even though some feature vector such as Gabor feature vector is relatively robust to variations of illumination. Also, illumination normalization techniques cannot eliminate illumination effects completely. In this paper, we propose an illumination-robust face recognition method based on the face Gabor intrinsic identity PCA model. We first analyze face Gabor feature vector space and construct a face Gabor intrinsic identity PCA model which is independent of illumination effects and propose a face recognition method based on it. Through experiments, it is shown that the proposed face recognition based on face Gabor intrinsic identity PCA model performs more reliably under various illuminations and pose environments.