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

The dimensionality of existing data make it difficult to deploy any information to identifyfeatures that discriminate between the classes of interest. Feature selection involves reducing the numberof features, removes irrelevant, noisy and redundant data without significantly decreasing the predictionaccuracy of the classifier. An efficient feature selection and classification technique for face recognitionis 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 techniquehas two purposes in this research: Selecting of the optimal feature subset and Selecting of the kernelparameters for SVM classifier. The input feature vector for the GAs-SVM are extracted by using theDiscrete Cosine Transform (DCT). We evaluate its efficiency compared to the recently proposed feature\selection algorithm based on mutual information. The results show that the proposed approach is\promising, it is able to select small subsets and still improve the classification accuracy