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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Semi-supervised Nearest Neighbor Discriminant Analysis Using Local Mean for Face Recognition
verfasst von : Caikou Chen, Pu Huang, Jingyu Yang
Erschienen in: Artificial Intelligence and Computational Intelligence
Verlag: Springer Berlin Heidelberg
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Feature extraction is the key problem of face recognition. In this paper, we propose a new feature extraction method, called semi-supervised local mean-based discriminant analysis (SLMNND). SLMNND aims to find a set of projection vectors which respect the discriminant structure inferred from the labeled data points, as well as the intrinsic geometrical structure inferred from both labeled and unlabeled data points. Experiments on the famous ORL and AR face image databases demonstrate the effectiveness of our method.