Gabor fused to 2DPCA for face recognition

Automatic visual recognition of human faces is an extremely attractive research subjects. It is motivated by the wide range of commercial and law enforcement application. In the last thirty years numerous algorithms for face recognition have been developed, for detailed surveys see. The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-Dimensional PCA (2DPCA) was proposed for human face representation and recognition. In this paper, we use the phase and magnitude of Gabor's representations of the face as a new representation followed by a face recognition algorithm, based on the 2D principal component Analysis approach. The performance of the proposed algorithm is tested on the public and largely used databases of FRGCv2 face and ORL. Experimental results on databases show that the use of 2D PCA, can achieve promising results, easier to evaluate the covariance matrix accurately and less time is required to computational.