Comparison between type-1 fuzzy membership functions for sign language applications

The paper presents a comparison between different membership functions based type-1 fuzzy set for automatic hand gesture recognition for American Sign Language recognition. First pre-processing of the images is done using skin color based segmentation, morphological operations and to extract the hand gesture image from the background, Sobel edge detection technique is performed. Then the image is sub-divided into 9 quadrants and for each quadrant, the area enclosed by the image in that specific quadrant is calculated. Based on areas from 15 images for a particular quadrant, the Gaussian three membership curves triangular, trapezoidal and Gaussian are designed. Now for an unknown gesture, nine membership values are determined and the summation of these membership values produces the strength of the unknown hand gesture matched with a known gesture. The highest strength obtained for a known gesture is the desired result. Experimentally, it is found that trapezoidal membership function outperforms the other two membership functions and gives overall an accuracy of 85.83%.