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marginFace: A novel face recognition method by average neighborhood margin maximization
Authors:Fei Wang [Author Vitae]  Xin Wang [Author Vitae] [Author Vitae]  Changshui Zhang [Author Vitae] [Author Vitae]
Affiliation:a State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China
b School of Computing and Information Sciences, Florida International University, FL 33174, USA
c Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Abstract:We propose a novel appearance-based face recognition method called the marginFace approach. By using average neighborhood margin maximization (ANMM), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the global Euclidean structure of face space, ANMM aims at discriminating face images of different people based on local information. More concretely, for each face image, it pulls the neighboring images of the same person towards it as near as possible, while simultaneously pushing the neighboring images of different people away from it as far as possible. Moreover, we propose an automatic approach for determining the optimal dimensionality of the embedded subspace. The kernelized (nonlinear) and tensorized (multilinear) form of ANMM are also derived in this paper. Finally the experimental results of applying marginFace to face recognition are presented to show the effectiveness of our method.
Keywords:Face recognition  Discrimination  Neighborhoods
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