Maximum Margin Subspace Projections for Face Recognition

Traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) have been employed in many fields such as biometrics patter recognition. But those methods only effectively preserve the global Euclidean structure or local structure of data set. In this paper, a novel unsupervised subspace method called Maximum Margin Subspace Projection (MMSP) is proposed. MMSP aims at preserving the local structure on the data manifold while maximize the global information of the samples simultaneously by maximizing the margin between the global structure and local structure of data manifold. Thus two abilities of manifold learning and classification have been combined into the proposed of our MMSP algorithm. Extensive experimental results on face databases demonstrate the effectiveness of the proposed algorithm.