Performance Estimation for Face Recognition Using SVD
Prashant K. Jain, Bhupesh Gautam · 2012
This paper is presented here for applied theory of linear algebra calledsingular value decomposition (SVD) to digital image processing. Two specific areas of digital image processing are investigated and tested. One is digital image compression, and other is recognition. In this paper we are measuring the FAR & FRR at different Threshold Level using Singular Value Decomposition by LDA in Own Database. SVD method can transform matrix a into product USV T , which allows us to refactoring a digital image in three matrices. The using of singular values of such refactoring allows us to represent the image with a smaller set of values, which can preserve useful features of the original image, but use less storage space in the memory. To perform recognition with SVD, we treated the set of Training faces as vectors in a subspace, called face space, spanned by a small group of faces. The projection of a new image onto the base is then compared to the set of known faces to identify the face. All tests and experiments are carried using MATLAB as computing environment and programming language.