Kakajan Kakayev and Songül Albayrak, Yildiz Technical University, Turkey

In past years, there were a lot of researches made in order to provide more accurate and comfortable interaction between human and machine. Developing a system which recognizes human gestures, is an important study to improve interaction between human and machine. Sign language is a way of communication for hearing-impaired people which enables them to communicate among themselves and with other people around them. Sign language consists of hand gestures and facial expressions. During the past 20 years, researches were made to facilitate communication of hearing-impaired people with others. Sign language recognition systems are designed in various countries. This paper presents a sign language recognition system, which uses Kinect camera to obtain skeletal model. Our aim was to recognize expressions, which are used widely in Turkish Sign Language (TSL). For that purpose we have selected 15 words/expressions randomly (repeated 4 times each by 3 different signers) which belong to Turkish Sign Language. We have used 180 records in total. Videos are recorded using Microsoft Kinect Camera and Nui Capture. Joint angles and joint positions have been used as features of gesture and achieved close to 100% recognition rates.

Hidden Markov Model, Turkish Sign Language Recognition, Gesture Recognition, Microsoft Kinect, skeleton model