In this paper we consider the problem of recognizing dynamic human gestures in the context of humanmachine interaction. We are particularly interested to the so-called Natural User Interfaces, a new modality based on a more natural and intuitive way of interacting with a digital device. In our work, a user can interact with a system by performing a set of encoded hand gestures in front of a webcam. We designed a method that first classifies hand poses guided by a finger detection procedure, and then recognizes known gestures with a syntactic approach. To this purpose, we collected a sequence of hand poses over time, to build a linguistic gesture description. The known gestures are formalized using a generative grammar. Then, at runtime, a parser allows us to perform gesture recognition leveraging on the production rules of the grammar. As for finger detection, we propose a new method which starts from a distance transform of the hand region and iteratively scans such region according to the distance values moving from a fingertip to the hand palm. We experimentally validated our approach, showing both the hand pose classification and gesture recognition performances.

Good practices on hand gestures recognition for the design of customized NUI

MALAFRONTE, DAMIANO;Noceti, Nicoletta
2015

Abstract

In this paper we consider the problem of recognizing dynamic human gestures in the context of humanmachine interaction. We are particularly interested to the so-called Natural User Interfaces, a new modality based on a more natural and intuitive way of interacting with a digital device. In our work, a user can interact with a system by performing a set of encoded hand gestures in front of a webcam. We designed a method that first classifies hand poses guided by a finger detection procedure, and then recognizes known gestures with a syntactic approach. To this purpose, we collected a sequence of hand poses over time, to build a linguistic gesture description. The known gestures are formalized using a generative grammar. Then, at runtime, a parser allows us to perform gesture recognition leveraging on the production rules of the grammar. As for finger detection, we propose a new method which starts from a distance transform of the hand region and iteratively scans such region according to the distance values moving from a fingertip to the hand palm. We experimentally validated our approach, showing both the hand pose classification and gesture recognition performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/886992
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