Dynamic Hand Gesture Recognition Using Neural Networks

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Author(s) Parul Vashist | K.Hema
Pages 486-491
Volume 2
Issue 6
Date June, 2013

Gesture recognition is important for developing an attractive alternative to prevalent human–computer interaction modalities. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. Neural networks take a different approach to problem solving than that of conventional computers. In this paper we focus on these problems: How to adapt the hand model to specific target? How to establish correspondences and combine (fuse) image data from multiple cameras in a 3-D framework? How good an algorithm handles occlusions and performs in highly cluttered environment? How to interpret the semantic meanings of a hand gesture dynamically?

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