Saurav Bhattacharya’s research while affiliated with Jadavpur University and other places

Body gesture is the most important way of non-verbal communication for deaf and dumb people. Thus, a novel sign language recognition procedure is presented here where the movements of hands play a pivotal role for such kind of communications. Microsoft’s Kinect sensor is used to act as a medium to interpret such communication by tracking the movement of human body using 20 joints. A procedural approach has been developed to deal with unknown gesture recognition by generating in-order expression for AVL tree as a feature. Here, 12 gestures are taken into consideration, and for the classification purpose, kernel function-based support vector machine is employed with results to gesture recognition into an accuracy of 88.3%. The foremost goal is to develop an algorithm that act as a medium to human–computer interaction for deaf and dumb people. Here, the novelty lies in the fact that for gesture recognition in sign language interpretation, the whole body of the subject is represented using a hierarchical balanced tree (here AVL).

Body gesture is the most important way of non-verbal communication for deaf and dumb people. Thus, a novel sign language recognition procedure is presented here where the movements of hands play a pivotal role for such kind of communications. Microsoft’s Kinect sensor is used to act as a medium to interpret such communication by tracking the movement of human body using 20 joints. A procedural approach has been developed to deal with unknown gesture recognition by generating in-order expression for AVL tree as a feature. Here, 12 gestures are taken into consideration, and for the classification purpose, kernel function-based support vector machine is employed with results to gesture recognition into an accuracy of 88.3%. The foremost goal is to develop an algorithm that act as a medium to human–computer interaction for deaf and dumb people. Here, the novelty lies in the fact that for gesture recognition in sign language interpretation, the whole body of the subject is represented using a hierarchical balanced tree (here AVL).

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%.