Approximate string matching: A lightweight approach to recognize gestures with Kinect. (February 2017)

• Record Type: Journal Article
• Authors: Ibañez, Rodrigo; Soria, Álvaro; Teyseyre, Alfredo; Rodríguez, Guillermo; Campo, Marcelo
• Is Part Of: Pattern recognition. Volume 62(2017:Feb.)
• Page Start: 73
• Page End: 86
• Publication Date: 2017-02
• Subjects: Natural user interfaces -- Gesture recognition -- Machine learning -- Kinect -- Approximate string matching; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.08.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Innovative technologies, such as 3D depth cameras, promote the development of natural interaction applications in many domains among large audiences. In this context, supervised machine learning techniques have been proved to be a flexible and robust approach to perform high level gesture recognition from 3D joints provided by these depth cameras. This paper proposes a lightweight approach to recognize gestures with Kinect by utilizing approximate string matching. The proposed approach encodes the movements of the joints as sequences of characters in order to simplify the gesture recognition as a widely studied string matching problem. We evaluated our approach by applying other widespread used techniques in the research field. The experimental evaluations show that the proposed approach can obtain relatively high performance in comparison with the state-of-the-art machine learning techniques. These findings provide further evidence that our approach could be a viable strategy for recognizing gestures, even in devices with medium and low processing capability (e.g., smartphones, tablets, etc.). Abstract : Highlights: We proposed a lightweight approach to recognize gestures with Kinect, based on approximate string matching. We implemented two gesture recognition variants, based on string matching. We evaluated our approach by using the public MSRC-12 Kinect gesture dataset. We compared the accuracy and performance with other state-of-the-art gesture-recognition techniques. The experimental evaluations show that the proposed approach achieves better performance than the state-of-the-art algorithms.