Task-Specific Gesture Analysis in Real-Time Using Interpolated Views
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Abstract—Hand and face gestures are modeled using an appearance-based approach in which patterns are represented as a vector of similarity scores to a set of view models defined in space and time. These view models are learned from examples using unsupervised clustering techniques. A supervised learning paradigm is then used to interpolate view scores into a task-dependent coordinate system appropriate for recognition and control tasks. We apply this analysis to the problem of context-specific gesture interpolation and recognition, and demonstrate real-time systems which perform these tasks.
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Index Terms:
Gesture recognition, real-time image processing, expression analysis, view-based representation, spatio-temporal gestures.
Citation:
Trevor J. Darrell, Irfan A. Essa, Alex P. Pentland, "Task-Specific Gesture Analysis in Real-Time Using Interpolated Views," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 12, pp. 1236-1242, Dec. 1996, doi:10.1109/34.546259
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