Gesture Recognition Using Image Comparison Methods | Zendy

Philippe Dreuw | Zendy; Daniel Keysers | Zendy; Thomas Deselaers | Zendy; Hermann Ney | Zendy

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Gesture Recognition Using Image Comparison Methods

Author(s) -

Philippe Dreuw,

Daniel Keysers,

Thomas Deselaers,

Hermann Ney

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-32624-3

DOI - 10.1007/11678816_14

Subject(s) - gesture , artificial intelligence , computer science , hidden markov model , alphabet , computer vision , pattern recognition (psychology) , segmentation , image (mathematics) , gesture recognition , distortion (music) , amplifier , computer network , philosophy , linguistics , bandwidth (computing)

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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