American Sign Language recognition by using 3D geometric invariant feature and ANN classification

Communication between normal and disabled person has been developed in several researches. The hand gesture is one of important communication for the deaf, especially American Sign Language (ASL) which is used in order to represent each alphabet (A-Z). This paper aims to translate ASL from static postures. Besides, this research also designs the glove with six different colored markers and develops algorithm for alphabet classification. Moreover the system is set by two cameras in order to extract 3D coordinate points from each marker. There are three main important processes for algorithm consisting of marker detection by using Circle Hough Transform, computation of all feasible triangle area patches constructed from 3D coordinate triplet that is novel feature, and feature classification using feedforward backpropagation of Artificial Neural Network. The experimental result shows average of accuracy is 95 percent that is high performance and feasibility for proposed method.