Abstract
We introduce the use of appearance-based features, and tangent distance or the image distortion model to account for image variability within the hidden Markov model emission probabilities to recognize gestures. No tracking, segmentation of the hand or shape models have to be defined. The distance measures also perform well for template matching classifiers. We obtain promising first results on a new database with the German finger-spelling alphabet. This newly recorded database is freely available for further research.
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Dreuw, P., Keysers, D., Deselaers, T., Ney, H. (2006). Gesture Recognition Using Image Comparison Methods. In: Gibet, S., Courty, N., Kamp, JF. (eds) Gesture in Human-Computer Interaction and Simulation. GW 2005. Lecture Notes in Computer Science(), vol 3881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11678816_14
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DOI: https://doi.org/10.1007/11678816_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32624-3
Online ISBN: 978-3-540-32625-0
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