DocumentCode :
3309051
Title :
Application of Boolean Kernel Function SVM in Face Recognition
Author :
Cui, Kebin ; Du, Yingshuag
Author_Institution :
Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding
Volume :
1
fYear :
2009
fDate :
25-26 April 2009
Firstpage :
619
Lastpage :
622
Abstract :
SVM based on Boolean kernel function has outstanding performance in classifying, for the problem of face recognition, recognizing strategies based on MDNF and MPDNF Boolean kernel function SVM are Proposed. Firstly, Karhunen-Loeve transform is employed to get the representation basis of face image set, secondly, the extracted characteristics is translated into 0-1 format, thirdly, SVM based Boolean kernel function are used to classify. The face recognition experiments with ORL face databases show that the proposed methods led to significantly better recognition accuracy compared with traditional PCA method and linear SVM, between the proposed methods, the one based on MPDNF Boolean kernel function get better performance.
Keywords :
Boolean functions; Karhunen-Loeve transforms; face recognition; image classification; image representation; polynomials; support vector machines; Karhunen-Loeve transform; MPDNF Boolean kernel function; face recognition; image classification; image representation; monotone polynomial disjunctive normal form; support vector machine; Artificial intelligence; Computer science; Eigenvalues and eigenfunctions; Face recognition; Karhunen-Loeve transforms; Kernel; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines; Boolean kernel function; Karhunen-Loeve transform; face recognition; multi-classification; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-1-4244-4223-2
Type :
conf
DOI :
10.1109/NSWCTC.2009.172
Filename :
4908341
Link To Document :
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