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Biometric devices provide secure mechanism towards gaining access. One of the Biometric features is Face and the system implemented is Face Recognition system. The Classical Face Recognition System is implemented with Principal Component Analysis and is successful. PCA is a linear method of extracting the features in a lower dimension space and is severely affected by the Pose and surrounding illumination variation. To implement effective face recognition system, pose variation is to be considered and the problem is well addressed with Kernel PCA (nonlinear PCA). KPCA extracts features in a higher dimension space, there by the system is rugged to pose variation. The illumination variation is accounted for capture range of the front end device and its surrounding and is not dealt in KPCA. In this work Singular Value Decomposition is used to deal with surrounding illumination and wavelets are employed to aid the KPCA in capturing the Multi Scale Features there by making the System robust to pose and illumination variation. To show the performance, the proposed method is tested on YaleB, ORL Databases. The results obtained show the impact of the method and is compared with PCA, KPCA.