Real time sign language recognition using depth sensor. (2nd August 2019)

• Record Type: Journal Article
• Authors: Gangrade, Jayesh; Bharti, Jyoti
• Is Part Of: International journal of computational vision and robotics. Volume 9:Number 4(2019)
• Page Start: 329
• Page End: 339
• Publication Date: 2019-08-02
• Subjects: Indian sign gesture -- multi-class support vector machine -- human computer interaction -- pattern recognition -- Kinect sensor; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

Communication via gestures is a visual dialect utilised by deaf and hard-of-hearing (HoH) people group. This paper proposed a system for sign language recognition utilising human skeleton data provided from Microsoft's Kinect sensor to recognising sign gestures. The Kinect sensor generates the skeleton of a human body and distinguishes 20 joints in it. The proposed method utilises 11 out of 20 joints and extracts 35 novel features per frame, based on distances, angles and velocity involving upper body joints. Multi-class support vector machine classified the 35 Indian sign gestures in real time with accuracy of 87.6%. The proposed method is robust in cluttered environment and viewpoint variation.