Mohammad Reza Mohammadi - Electrical Engineering Sharif University of Technology
Emad Fatemizadeh - Electrical Engineering Sharif University of Technology Tehran,

The Local Binary Patterns (LBP) featureextraction method is a theoretically and computationally simpleand efficient methodology for texture analysis. The LBP operatoris used in many applications such as facial expression recognitionand face recognition. The original LBP is based on hardthresholding the neighborhood of each pixel, which makestexture representation sensitive to noise. In addition, LBP cannotdistinguish between a strong and a weak pattern. In order toenhance the LBP approach, Fuzzy Local Binary Patterns (FLBP)is proposed. In FLBP, any neighborhood does not representedonly by one code, but, it is represented by all existing codes withdifferent degrees. In FLBP, any fuzzy Intersection and Unionoperators may be used. In this study, the following operators areapplied and their results are compared together: Dot-Sum, Min-Max and normalized Min-Max. Based on the extensiveexperiments, the fuzzy Min-Max operators are more useful andcan improve the accuracy in the application of Facial ExpressionRecognition (FER) about 4% (i.e., form 82.98% to 86.88%).

component; Fuzzy Local Binary Patterns; Min-Max Operators; Dot-Sum Operators; Facial Expression Recognition Support Vector Machine