Biometric face recognition using randomfaces

This paper investigates the use of the Compressive Sensing (CS) technique to the classification issue. In this context, CS is used as a means to probe the nonlinear manifold on which faces under various illumination effects reside. The scheme of randomly sampled faces (Randomfaces) with nearest neighbor classifier are compared with two classical feature extraction approaches, as Eigenfaces and Fisherfaces. It is shown that randomfaces outperform the eigenface approach in classifying faces under illumination disturbances and their performance approaches that of the Fisherfaces.