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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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Recognition rates and corresponding dimensions of MPDA and other algorithms on the FERET database when l = 3.
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fig5: Recognition rates and corresponding dimensions of MPDA and other algorithms on the FERET database when l = 3.

Mentions: On the FERET database, the first l (=3, 4) images of each person are selected as the training set, and the rest 7-l images are taken as the test set. Figures 5 and 6 show the comparison between the recognition rate of MPDA and other algorithms, when training sample quantity on the FERET database is separately 3 or 4 and when the projection axis of algorithm is different. Table 3 is a comparison between the best recognition rate of MPDA and other algorithms on the three face databases. It is shown by Figures 5 and 6 that, when the projection axis quantity is higher than 30, MPDA's recognition rate is always better than other algorithms. Moreover, from Table 3, it can be seen that MPDA achieves a much higher recognition rate than other algorithms, which further demonstrates the effectiveness of the proposed algorithm, as well as the theoretical analysis based on the Yale database.


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

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

Recognition rates and corresponding dimensions of MPDA and other algorithms on the FERET database when l = 3.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Recognition rates and corresponding dimensions of MPDA and other algorithms on the FERET database when l = 3.
Mentions: On the FERET database, the first l (=3, 4) images of each person are selected as the training set, and the rest 7-l images are taken as the test set. Figures 5 and 6 show the comparison between the recognition rate of MPDA and other algorithms, when training sample quantity on the FERET database is separately 3 or 4 and when the projection axis of algorithm is different. Table 3 is a comparison between the best recognition rate of MPDA and other algorithms on the three face databases. It is shown by Figures 5 and 6 that, when the projection axis quantity is higher than 30, MPDA's recognition rate is always better than other algorithms. Moreover, from Table 3, it can be seen that MPDA achieves a much higher recognition rate than other algorithms, which further demonstrates the effectiveness of the proposed algorithm, as well as the theoretical analysis based on the Yale 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