Abstract
The face recognition performance of linear discriminant analysis (LDA) is considerably reduced by illumination and scale changes. Actually, performance of LDA is significantly affected by the number of images of each individual in the training set. Hence, the idea presented in this paper is to generate new images with illumination and scale changes, from the available frontal images in the training set, and add to training set such that the recognition rate of LDA is maximised over both the training and test sets. A continuous genetic algorithm is used to find the optimal sets of illumination and scale change parameters that make the success of LDA invariant to these facial variations. Compared to the application of LDA over the initially given face database, experimental evaluations demonstrated that the performance of LDA is significantly enhanced over the test set for illumination and scale changes, without decreasing the success for other test cases.
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