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Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper,...
In this paper, we describe a feature extraction algorithm called discriminant uncorrelated locality aware embedding, DULAM for short, which is based on LPP (locality preserving projection). LPP can preserve the local structure of the data, but does not take the class information into account, besides, the extracted feature might be highly correlated. To overcome these drawbacks, DULAM is proposed,...
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