Abstract
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.
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Chen, C., Huang, P., Yang, J. (2010). Semi-supervised Nearest Neighbor Discriminant Analysis Using Local Mean for Face Recognition. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_39
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DOI: https://doi.org/10.1007/978-3-642-16530-6_39
Publisher Name: Springer, Berlin, Heidelberg
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