Face recognition based on LBP and orthogonal rank-one tensor projections

In this paper, a novel framework for face recognition based on discriminatively trained orthogonal rank-one tensor projections (ORO) and local binary pattern (LBP) is proposed. LBP is an efficient method for extracting shape and texture information and it is robustness to illumination and expression, while ORO has been successful in appearance based face recognition by finding orthogonal tensors. Accordingly, we propose to reduce the dimension of LBP by ORO. Moreover, we propose to update the k-nearest neighbors when each subspace has been obtained in ORO. The experiments demonstrate that the new ORO can be stabilized more quickly and obtain higher correct rate. Finally, because the computation of LBP is simple and the size of compress matrix of ORO is small, this algorithm is easy to be applied to embedded application.