Fuzzy linear and nonlinear discriminant analysis algorithms for face recognition

Article type: Research Article

Authors: Chougdali, Khalid^{; *} | Jedra, Mohamed | Zahid, Noureddine

Affiliations: Laboratory of Conception and Systems, Faculty of Sciences AGDAL, Avenue Ibn Batouta, B.P.1014, Rabat, Morocco

Correspondence: [*] Corresponding author. Tel.: +212 60 40 27 40; E-mail: [email protected].

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.

Keywords: Face recognition, kernel methods, scatter-difference discriminant analysis, fuzzy logic

DOI: 10.3233/IDA-2009-0385

Journal: Intelligent Data Analysis, vol. 13, no. 4, pp. 657-669, 2009