marginFace: A novel face recognition method by average neighborhood margin maximization
Pattern RecognitionPublished 5 May 2009
Fei Wang, Xin Wang, Daoqiang Zhang, Changshui Zhang, Tao Li
Citations50
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
An automatic approach for determining the optimal dimensionality of the embedded subspace of the ANMM and the experimental results of applying marginFace to face recognition are presented to show the effectiveness of the method.
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
Computer Science
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