<title>Dynamic gesture recognition using neural networks: a fundament for advanced interaction construction</title>

Abstract

Interaction in Virtual Reality environments is still a challenging task. Static hand posture recognition is currently the most common and widely used method for interaction using glove input devices. In order to improve the naturalness of interaction, and thereby decrease the user-interface learning time, there is a need to be able to recognize dynamic gestures. Dynamic Gesture Recognition (DGR) is difficult for various reasons. The large variations in the speed of execution of various phases of a gesture is one such reason. Another is the quality and positions of the physical properties describing a gesture themselves. These problems are then exaggerated by the differences which arise when various people attempt the same gesture, as well as when the same person attempts repeated executions of the same gesture. Other factors effecting the difficulty of DGR are the emotional state of the person doing the gesture and the accuracy of the input device used. And finally, a large amount of data has to be processed in real time because of large variances in the length of time to execute a gesture. In this paper we describe our approach to overcoming the difficulties of DGR using neural networks. Backpropagation neural networks have already proven themselves to be appropriate and efficient for posture recognition. However, the extensive amount of data involved in DGR requires a different approach. Because of features such as topology preservation and automatic-learning, Kohonen Feature Maps are particularly suitable for the reduction of the high dimensional data space which is the result of a dynamic gesture, and are thus implemented for this task.

Real time gesture recognition using continuous time recurrent neural networks

“…Lastly, these fuzzy predictors do not present any time dynamics. Therefore, these problems and the search for a more general predictor were the reasons that motivated us to look for another kind of predictors Neural networks have been used in previous research to perform gesture classification [19,4,16]. In these approaches complex hierarchical neural networks are often used (Kohonen map combined with a recurrent network in [4], multinetwork approach in [16]).…”

Recognition of human actions using motion history information extracted from the compressed video

“…There are few works reported in literature which use neural networks for gesture recognition [19,7]. Boehm et al [7] used Kohonen Feature Maps (KFM) [18] for recognizing dynamic gestures.…”

“…Boehm et al [7] used Kohonen Feature Maps (KFM) [18] for recognizing dynamic gestures. Oliver et al [23] proposed a system for modeling and recognizing human behaviors in a visual surveillance task.…”

Hand posture recognition in a body-face centered space

“…Neural networks, such as discriminant models [5] or Kohonen features maps models [1], have previously been applied to hand posture recognition. In this work, we propose to use a neural network model already applied to face detection: the constrained generative model (CGM) [3] ( Figure 3).…”