A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

Rodrigo Ibañez (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Alvaro Soria (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Alfredo Raul Teyseyre (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Luis Berdun (ISISTAN (UNICEN-CONICET) Research Institute, Argentina) and Marcelo Ricardo Campo (ISISTAN (UNICEN-CONICET) Research Institute, Argentina)
DOI: 10.4018/978-1-7998-2460-2.ch056

Abstract

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.
Chapter Preview
Top

Background

In the literature, there are numerous approaches to gesture recognition from human body movements captured by video cameras. As mention in (Gavrila, 1999), the ability to recognize humans and their activities by vision is crucial for a machine to interact intelligently and effortlessly with a human-inhabited environment. Over the years, there has been strong interest in human movement from a wide variety of disciplines. In psychology, there have been the classic studies on human perception by Johansson (1973) or, in the hand gesture area, how humans use and interpret gestures (McNeill, 1992). In kinesiology the goal has been to develop models of the human body that explain how it works mechanically and how one might increase its movement efficiency (Calvert & Chapman, 1994). Computer graphics has dealt with the synthesis of human movement and some of the issues have been how to specify spatial interactions and high-level tasks for the human models; see (Badler, Phillips, & Webber, 1993; Badler & Smoliar, 1979; Magnenat-Thalmann & Thalmann, 1990). For a review of the state of the art in human movement recognition in general see (Gavrila, 1999; Aggarwal & Cai, 1999; Yu, Cheng, Cheng, & Zhou, 2004; Weinland, Ronfard, & Boyer, 2011; Turaga, Chellappa, Subrahmanian, & Udrea, 2008); for facial expressions see (Mitra, & Acharya, 2007); for hand gestures see (Pavlovic, Sharma, & Huang, 1997; Wachs, Kölsch, Stern, & Edan, 2011).

Complete Chapter List

Search this Book:
Reset