Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13213
Title: Discriminant subclass-center manifold preserving projection for face feature extraction
Authors: Lan, C
Jing, X
Zhang, D 
Gao, S
Yang, J
Keywords: Discriminant subclass-center manifold preserving projection (DSMPP)
Face feature extraction
Manifold learning
Subclass-center neighborhood structure
Issue Date: 2011
Source: Proceedings - International Conference on Image Processing, ICIP, 2011, p. 3013-3016 How to cite?
Abstract: Manifold learning is an effective feature extraction technique, which seeks a low-dimensional space where the manifold structure, in terms of local neighborhood, of the data set can be well preserved. A typical manifold learning method constructs a local neighborhood centered at individual samples. In this paper, we propose to construct local neighborhoods that centered at subclass centers, and seek an embedded space where such neighborhood is well preserved. We show from a probability perspective that, neighbors of a subclass center would contain more intra-class data than inter-class data, which may be desirable for discrimination. Meanwhile, we simultaneously enhance the discriminative power of extracted features by maximizing the Fisher ratio of embedded data based on subclass centers. Experimental results on CAS-PEAL and FERET face databases demonstrate that our proposed approach is more effective than most typical manifold learning methods and their supervised extensions in classification performance.
Description: 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, 11-14 September 2011
URI: http://hdl.handle.net/10397/13213
ISBN: 9781457713033
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6116297
Appears in Collections:Conference Paper

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