An Improved Genetic Algorithm Based Weight Optimized RBF Kernel System for Face Recognition
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Face recognition system is one of many biometric authentication systems which are used for authentication purpose. It provides an effective way for authentication with varied security features. Management of high dimension data is problem which arrives frequently in face recognition. In this paper a new method is proposed for biometric authentication based face recognition which is general and efficient approach using radial basis function (RBF) kernel to manage small training sets of high dimension. In order to avoid over fitting and reduce the computational complexity, face features are first extracted by the singular value decomposition (SVD) method. After that resulting features were further processed by the Fisher’s linear discriminate (FLD) technique to acquire lower-dimensional discriminate features. Data mining techniques have been widely used in intruder detection decision support systems to obtain good accuracy. A fast learning genetic algorithm (GA) is used to train the RBF kernels so that the aspects of the search space are significantly reduced. Results will be obtained after conducting simulations on ORL database, which are more precise in classification and learning efficiency
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RBF Kernels; Face Recognition System; Optimization; Genetic Algorithm