Two dimension nonnegative partial least squares for face recognition

For benefiting from incorporating the class information, partial least squares (PLS) and its two dimension version (2DPLS) have been widely employed in face recognition when extracting principal components. However, currently popular statistic methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), only learn holistic, not parts-based, representations which ignore available local features for face recognition. In this paper, we propose a novel approach to extract the facial features called two dimension nonnegative partial least squares (2DNPLS). Our approach can grab the local features via adding non-negativity constraint to the 2DPLS, and can also reserve the advantages of 2DPLS, which are both inherent structure and class information of images. For evaluating our approach's performance, a series of experiments were conducted on two famous face image databases include ORL and Yale face databases, which demonstrate that our proposed approach outperforms the compared state-of-art algorithms.