Discriminant Improved Local Tangent Space Alignment with Application to Face Recognition

摘要

Improved Local Tangent Space Alignment (ILTSA) is a recent nonlinear dimensionality reduction method but there exists the out-of-sample problem. In this paper, based on linearization and discriminant extension of ILTSA, a novel feature extraction method named Discriminant Improved Local Tangent Space Alignment (DILTSA) is proposed. Based on maximum neighborhood margin criterion and ILTSA, DILTSA can preserve both local within-class and between-class geometry structures. Experimental results on ORL and UMIST face databases demonstrate the effectiveness of the proposed face recognition method.