Abstract
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.
Highlights
Face recognition (FR) has played a very important role in multimedia based applications
Experiments are carried out and the results confirm that the proposed approaches can effectively improve the system performance in terms of face classification and reconstruction
A new compression strategy has been proposed based on the projection matrix optimization
Summary
Face recognition (FR) has played a very important role in multimedia based applications. As it involves storing and transmitting high dimensional images, image compression techniques such as JPEG and JPEG2000 are used to alleviate the problem [2, 3]. Users have to decompress (reconstruct) images and extract the image features for classification Such a procedure usually requires a lot of computations and makes the systems expensive. It can be much simplified if the images can be acquired using compressive sensing, which outputs features of images extracted directly, and the classification and reconstruction are done with the extracted features.
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