Fuzzy linear and nonlinear discriminant analysis algorithms for face recognition

Authors:
Khalid Chougdali;Mohamed Jedra;Noureddine Zahid
Affiliations:
(Correspd. Tel.: +212 60 40 27 40/ E-mail: chougdali@yahoo.fr) Laboratory of Conception and Systems, Faculty of Sciences AGDAL, Avenue Ibn Batouta, B.P.1014, Rabat, Morocco;Laboratory of Conception and Systems, Faculty of Sciences AGDAL, Avenue Ibn Batouta, B.P.1014, Rabat, Morocco;Laboratory of Conception and Systems, Faculty of Sciences AGDAL, Avenue Ibn Batouta, B.P.1014, Rabat, Morocco
Venue:
Intelligent Data Analysis
Year:
2009

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Abstract

This paper presents two novels algorithms based on fuzzy logic and discriminant analysis for face recognition. The first one is a fuzzy extension of a linear discriminant analysis algorithm namely LDA/QR and the second one is a fuzzy extension of kernel scatter-difference based discriminant analysis (KSDA) algorithm. They can deal with small sample size and nonlinear problems which degrade the performance of face recognition system. Experimental results on the ORL and the extended Yale B face databases show that the two proposed approaches, using fuzzy logic, achieves a better performance in face recognition compared with their original versions.