Nearest orthogonal matrix representation for face recognition
This paper presents a simple but effective method for face recognition, named nearest orthogonal matrix representation (NOMR). Specifically, the specific individual subspace of each image is estimated and represented uniquely by the sum of a set of basis matrices generated via singular value decomposition (SVD), i.e. the nearest orthogonal matrix (NOM) of original image. Then, the nearest neighbor criterion is introduced for recognition. Compared with the current specific individual subspace based methods (e.g. the sparse representation based classifier, the linear regression based classifier and so on), the proposed NOMR is more robust for alleviating the effect of illumination and heterogeneous (e.g. sketch face recognition), and more intuitive and powerful for handling the small sample size problem. To evaluate the performance of the proposed method, a series of experiments were performed on several face databases: Extended Yale B, CMU-PIE, FRGCv2, AR and CUHK Face Sketch database (CUFS). Experimental results demonstrate that the proposed method achieves encouraging performance compared with the state-of-the-art methods.
face recognition image representation pattern classification singular vector decomposition (svd) sketch image recognition