Robust Singular Value Decomposition Algorithm for Unique Faces (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2018)
It has been read and also seen by physical encounters that there found to be seven near resembling humans by appearance .Many a times one becomes confused with respect to identification of  such near resembling faces when one encounters them. The  recognition  of  familiar  faces  plays  a  fundamental  role  in  our  social interactions. Humans  are  able  to  identify  reliably  a  large  number  of  faces  and psychologists  are  interested  in  understanding  the  perceptual  and  cognitive mechanisms  at  the  base  of  the  face  recognition  process. As it is needed that an automated face recognition system should be faces specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. The paper shows that the most resembling faces can be recognized by having a unique value per face under different variations. Certain image transformations, such as intensity negation, strange viewpoint changes,  and  changes  in  lighting  direction  can  severely  disrupt  human  face recognition. It has been said again and again by research scholars that SVD algorithm is not good enough to classify faces under large variations but this paper proves that the SVD algorithm is most robust algorithm and can be proved effective in identifying faces under large variations as applicable to unique faces. This paper works on these aspects and tries to recognize the unique faces by applying optimized SVD algorithm.