Invariant Image Recognition by Zernike Moments
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
Invariant handwritten Chinese character recognition using fuzzy min-max neural networks
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
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In this paper a new method is proposed for face recognition when subject is illuminated from left and right direction in a fixed pose named "generalized ring averaging". We have proposed new features called "generalized ring averaging" features, which is extension to ring features. Ring features are invariant to rotation and used for binary images only. Proposed features are invariant to direction of illumination (left and right) and used for gray scale images. Well known Fuzzy min-max neural network classifier is used for recognition purpose. The proposed method is found better than one of the most popular method used for face recognition called "eigenfaces", in terms of percentage recognition rate, when compared with same dimensionality of feature vector. The proposed method requires less time to extract features than eigenfaces and recall time per pattern is found comparable to eigenfaces. However, the proposed method is suitable only for frontal poses and its little variants, which are very close to frontal pose and only for left and right direction of illumination by keeping pose and illumination strength around constant.