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IJMLC 2014 Vol.4(1): 24-30 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.381

Interval Type-2 Fuzzy Logic to the Treatment of Uncertainty in 2D Face Recognition Systems

Saad M. Darwish and Ali H. Mohammed

Abstract—Uncertainty is an intrinsic part of intelligent systems used in face recognition applications. The use of new methods for handling inaccurate information about facial features is of fundamental importance. This paper deals with the design of intelligent 2D face recognition system using interval type-2 fuzzy logic for diminishing the effects of uncertainty formed by variations in light direction, face pose and facial expression. Built on top of the well-known fisher face method, our system employs type-2 fuzzy set to compute fuzzy within and in-between class scatter matrices of fisher’s linear discriminant. This employment makes the system able to improve face recognition rates as the results of reducing the sensitivity to substantial variations between face images. Type-2 Fuzzy Sets (T2FSs) have been shown to manage uncertainty more effectively than Type-1 Fuzzy Sets (T1FS), because they provide us with more parameters that can handle environments where it is difficult to define an exact membership function for a fuzzy set. Experimental results for YALE and ORL face databases are given, which show the effectiveness of the suggested system for face recognition and also illustrate high accuracy when compared with other methods.

Index Terms—Face recognition, interval type-2 fuzzy logic, soft computing, image processing.

Saad M. Darwish is with the Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby 21526, P.O. Box 832, Alexandria, Egypt (e-mail: saad.saad@alexu.edu.eg).
Ali H. Mohammed is with the Department of Computer, Ministry of Education, Iraq (e-mail: ali_mustfa883@gmail.com).


Cite:Saad M. Darwish and Ali H. Mohammed, "Interval Type-2 Fuzzy Logic to the Treatment of Uncertainty in 2D Face Recognition Systems," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 24-30, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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