SVD-Based Face Recognition

Projection-based face recognition has been widely studied during the past two decades. One of the problems is to require a huge storage space to save the face features obtained from training faces. We propose an SVD-based face retrieval system which requires less memory than the PCA, 2DPCA, Fisher, and 2DFisher approaches. The algorithm tested on the famous ORL (AT&T) face database, consisted of 400 112 x 92 gray level face images equally contributed by 40 subjects, achieves 97.5% recognition rate of retrievals.