Face Recognition Based on Optimized Projections for Distributed Intelligent Monitoring Systems

Compressive sensing (CS), as a new theory of signal processing, has found many applications. This paper deals with a CS-based face recognition system design. A novel framework, called projection matrix optimization- (PMO-) based compressive classification, is proposed for distributed intelligent monitoring systems. Unlike the sparse preserving projection (SPP) approach, the projection matrix is designed such that the coherence between different classes of faces is reduced and hence a higher recognition rate is expected. The optimal projection matrix problem is formulated as identifying a matrix that minimizes the Frobenius norm of the difference between a given target Gram and that of the equivalent dictionary. A class of analytical solutions is derived. With the PMO-based CS system, two frameworks are proposed for compressive face recognition. Experiments are carried out with five popularly utilized face databases (i.e., ORL, Yale, Yale Extend, CMU PIE, and AR) and simulation results show that the proposed approaches outperform those existing compressive ones in terms of the recognition rate and reconstruction error.