An Approach to Glove-Based Gesture Recognition

Authors:
Farid Parvini;Dennis Mcleod;Cyrus Shahabi;Bahareh Navai;Baharak Zali;Shahram Ghandeharizadeh
Affiliations:
Computer Science Department, University of Southern California, Los Angeles, 90089-0781;Computer Science Department, University of Southern California, Los Angeles, 90089-0781;Computer Science Department, University of Southern California, Los Angeles, 90089-0781;Computer Science Department, University of Southern California, Los Angeles, 90089-0781;Computer Science Department, University of Southern California, Los Angeles, 90089-0781;Computer Science Department, University of Southern California, Los Angeles, 90089-0781
Venue:
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Year:
2009

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Abstract

Nowadays, computer interaction is mostly done using dedicated devices. But gestures are an easy mean of expression between humans that could be used to communicate with computers in a more natural manner. Most of the current research on hand gesture recognition for Human-Computer Interaction rely on either the Neural Networks or Hidden Markov Models (HMMs). In this paper, we compare different approaches for gesture recognition and highlight the major advantages of each. We show that gestures recognition based on the Bio-mechanical characteristic of the hand provides an intuitive approach which provides more accuracy and less complexity.