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GA-SVM and Mutual Information Based Frequency Feature Selection for Face Recognition

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Executive Summary

The dimensionality of existing data makes it difficult to deploy any information to identify features that discriminate between the classes of interest. Feature selection involves reducing the number of features, removes irrelevant, noisy and redundant data without significantly decreasing the prediction accuracy of the classifier. An efficient feature selection and classification technique for face recognition is presented in this paper. Genetic Algorithms (GAs) for feature selection and Support Vector Machine (SVM) for classification are incorporated in the proposed technique. The proposed GAs-SVM technique has two purposes in this research: Selecting of the optimal feature subset and Selecting of the kernel parameters for SVM classifier.

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