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Membership-degree preserving discriminant analysis with applications to face recognition.

Yang Z, Liu C, Huang P, Qian J - Comput Math Methods Med (2013)

Bottom Line: In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data.The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied.Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

ABSTRACT
In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

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Related in: MedlinePlus

10 images of one person on the ORL database.
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fig1: 10 images of one person on the ORL database.

Mentions: As human face image can be up to millions of dimensions, while human's vision is up to 3 dimensions at best, in order to more visually figure out the internal connection between the data, to compare the difference and performance of these algorithms, in the research, the four algorithms of MPDA, LPP, UDP, and MFA are selected to take image two-dimensional visualization test. The ORL database (http://www.cam-orl.co.uk) contains 400 different images from 40 subjects: each subject has 10 images. For some subjects, the images were taken at different times, with varying the lighting, facial expressions (open/closed eyes, smiling/not smiling), and facial details (glasses/no glasses). All images are grayscale and resized to a resolution of 32 × 32. Figure 1 shows 10 images of a person on the ORL database.


Membership-degree preserving discriminant analysis with applications to face recognition.

Yang Z, Liu C, Huang P, Qian J - Comput Math Methods Med (2013)

10 images of one person on the ORL database.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814106&req=5

fig1: 10 images of one person on the ORL database.
Mentions: As human face image can be up to millions of dimensions, while human's vision is up to 3 dimensions at best, in order to more visually figure out the internal connection between the data, to compare the difference and performance of these algorithms, in the research, the four algorithms of MPDA, LPP, UDP, and MFA are selected to take image two-dimensional visualization test. The ORL database (http://www.cam-orl.co.uk) contains 400 different images from 40 subjects: each subject has 10 images. For some subjects, the images were taken at different times, with varying the lighting, facial expressions (open/closed eyes, smiling/not smiling), and facial details (glasses/no glasses). All images are grayscale and resized to a resolution of 32 × 32. Figure 1 shows 10 images of a person on the ORL database.

Bottom Line: In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data.The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied.Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

ABSTRACT
In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

Show MeSH
Related in: MedlinePlus