Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition

In this paper, we propose a novel orthogonal complete discriminant locality preserving projections for facial feature extraction and recognition (OCDLPP). All training samples are projected into the range of a so-called locality preserving total scatterto reduce dimensionality without loss of discriminative information. The transformation matrix of OCDLPP is orthogonal and is found simultaneously using QR decomposition technique. Moreover, a feasible and effective procedure is proposed to alleviate the computational burden of high dimensional matrix for typical face image data. Experiments results on the ORL, Yale, FERET and PIE face databases show the effectiveness of the proposed OCDLPP.