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: firstname.lastname@example.org).
Ali H. Mohammed is with the Department of Computer, Ministry of Education, Iraq (e-mail: email@example.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.