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Hand gesture recognition and spotting in uncontrolled environments based on classifier weighting
© 2015 IEEE. Pure appearance based Hand Gesture Recognition and Spotting in uncontrolled environments are challenging tasks due to the uncontrolled scene settings include: multiple hand regions in the scene; background moving objects; scale, speed and location variations of the gesture trajectories; changing lighting conditions and frontal occlusions. An appearance based method based on a novel classifier weighting scheme is proposed in this paper for hand gesture recognition and spotting in uncontrolled environments. The method is capable of producing decent performance with the presence of all the aforementioned challenges. Two databases are used for evaluating the proposed method, the Palm Graffiti Digits Database and the Warwick Hand Gesture Database. The experimental results demonstrate that the proposed method can deal with the challenges from uncontrolled environments without any prior knowledge and enhance the performance of the initial classifier.
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Event
Image Processing. International Conference (2015 : Quebec City, Canada)Pagination
3082 - 3086Publisher
IEEELocation
Quebec City, CanadaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-09-27End date
2015-09-30ISSN
1522-4880ISBN-13
9781479983391Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
ICIP 2015 : Proceedings of the IEEE International Conference on Image ProcessingUsage metrics
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