A 2DPCA Based Semi-Supervised Learning and its Application on Face Recognition

Hao Ge Zeng; Li Zhu Zhan; Xi Yang Yang

doi:10.4028/www.scientific.net/AMM.543-547.2171

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547A 2DPCA Based Semi-Supervised Learning and its...

A 2DPCA Based Semi-Supervised Learning and its Application on Face Recognition

Abstract:

A Mahalanobis distance based semi-supervised fuzzy clustering model is presented in this paper, whose objective function has a good explanation on how the labeled and unlabeled data are used in finding the underlying structure of matrix data. The iterative algorithm to solve this model is given. This algorithm can directly deal with matrix data like face images. We use 2DPCA on both row and column directions to reduce the dimension of image faces. The experimental result shows that using 2DPCA and semi-supervised algorithms can have a fairly good recognition rate if enough labeled data are given.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

2171-2174

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.2171

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Online since:

March 2014

Authors:

Hao Ge Zeng*, Li Zhu Zhan, Xi Yang Yang

Keywords:

2DPCA, Face Recognition, Semi-Supervised

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* - Corresponding Author

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