Limits...
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.

Show MeSH

Related in: MedlinePlus

Seven images of one person in the FERET database.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3814106&req=5

fig4: Seven images of one person in the FERET database.

Mentions: The FERET database (http://en.wikipedia.org/wiki/FERET_database) contains 14126 images from 1199 individuals. In our experiments, a subset which contains 1400 images of 200 individuals (wherein each individual has seven images) is selected. The subset involves variations in facial expression, illumination, and pose. Each image is grayscale, manually cropped, and resized to 80 × 80. Figure 4 shows some sample images of one person from the FERET database.


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

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

Seven images of one person in the FERET database.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: Seven images of one person in the FERET database.
Mentions: The FERET database (http://en.wikipedia.org/wiki/FERET_database) contains 14126 images from 1199 individuals. In our experiments, a subset which contains 1400 images of 200 individuals (wherein each individual has seven images) is selected. The subset involves variations in facial expression, illumination, and pose. Each image is grayscale, manually cropped, and resized to 80 × 80. Figure 4 shows some sample images of one person from the FERET 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