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Contemporary society is highly networked and thus biometrics based surveillance has paramount importance for various security based reasons. The automatic, remote and robot vision based system are being deployed in a large way [11, 12]. The success of these schemes is highly dependent on robust algorithms for both face and object recognition. In this paper, we propose a very robust approach to face/object recognition based on Singular Value Decomposition (SVD). We first provide technical reasons to substantiate the claims made by T.Yuan et al [1], then we provide appropriate reasons for the robust behavior of SVD and finally, we corroborate the proposed concepts through extensive experiments on two standard databases which includes face and objects under both clean and noise conditions.

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