[1] Luo Lin, Zou Cairong, Yang Fengfan,. Modified algorithm of principal component analysisfor face recognition [J]. Journal of Southeast University (English Edition), 2006, 22 (1): 26-30. [doi:10.3969/j.issn.1003-7985.2006.01.006]

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Modified algorithm of principal component analysisfor face recognition()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 1

Page:

26-30

Research Field:

Electronic Science and Engineering

Publishing date:

2006-03-20

Info

Title:

Modified algorithm of principal component analysisfor face recognition

Author(s):

Luo Lin¹;  Zou Cairong¹;  Yang Fengfan²

¹Department of Radio Engineering, Southeast University, Nanjing 210096, China
²Department of Electronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Keywords:

face recognition;  principal component analysis;  linear discriminant analysis

PACS:

TN391

DOI:

10.3969/j.issn.1003-7985.2006.01.006

Abstract:

In principal component analysis(PCA)algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA(MPCA)algorithm is proposed.The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation.The Yale face database and Yale face database B are used to verify the method.The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis(LDA)approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.

References:

[1] Kirby M, Sirovich L.Application of the Karhunen-Loeve procedure for the characterization of human faces [J].IEEE Trans on Pattern Analysis and Machine Intelligence, 1990, 12(1):103-108.
[2] Swets D L, Weng J J.Using discriminant eigenfeature for image retrieval [J].IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(8):831-836.
[3] Belhumeour P N, Hespanha J P, Kriegman D J.Eigenface vs.fisherfaces:recognition using class specific linear projection [J].IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
[4] Zhao W, Chellappa R, Krishnaswamy A.Discriminant analysis of principal components for face recognition [C]//Proc of IEEE International Conference on Automatic Face and Gesture Recognition.Nara, Japan, 1998: 336-341.
[5] Martinez A M, Kak A C.PCA versus LDA [J].IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(2):228-233.
[6] Pentland A, Starner T, Etcoff N, et al.Experiments with eigenfaces [C]//Proc of Looking at People Workshop, International Joint Conference on Artificial Intelligence.Chamberry, France, 1993: 1-6.
[7] Moon H, Phillips P J.Analysis of PCA-based face recognition algorithms[C]//Bowyer K J, Phillips P J.Empirical Evaluation Techniques in Computer Vision.California:IEEE Computer Science Press, 1998: 57-71.

Memo

Memo:

Biography: Luo Lin(1969—), female, doctor, associate professor, luolin@seu.edu.cn.

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