Tangent space discriminant analysis for feature extraction

In this paper, a novel method called tangent space discriminant analysis is proposed for dimensionality reduction and feature extraction. Differing from the recently proposed manifold learning methods completely operating on raw feature space, TSDA completely uses the local tangent space to represent the local within-class geometry and local between-class geometry. Assume that the face images of different people reside on different intrinsically low-dimensional sub-manifolds, TSDA is developed to preserve the locality of each sub-manifold and simultaneously maximize the local separability of different sub-manifolds by using local tangent space alignment. Experimental results show that TSDA achieves higher recognition rates than a few the state-of-the-art techniques.