Hand gesture recognition using input-output hidden Markov models

A new hand gesture recognition method based on input-output hidden Markov models is presented. This method deals with the dynamic aspects of gestures. Gestures are extracted from a sequence of video images by tracking the skin-color blobs corresponding to the hand into a body-face space centered on...

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Bibliographic Details
Published inProceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) pp. 456 - 461
Main Authors Marcel, S., Bernier, O., Viallet, J.-E., Collobert, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer vision
Exact sciences and technology
Face detection
Face recognition
Hidden Markov models
Image segmentation
Neural networks
Pattern recognition. Digital image processing. Computational geometry
Pixel
Recurrent neural networks
Skin
Telecommunications
Computer vision
Recurrent neural networks
Tracking
Image sequence
Gesture
Tracking(movable target)
Video technique
Markov model
Symbolic computation
Hand
Recognition
Input output model
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Summary:A new hand gesture recognition method based on input-output hidden Markov models is presented. This method deals with the dynamic aspects of gestures. Gestures are extracted from a sequence of video images by tracking the skin-color blobs corresponding to the hand into a body-face space centered on the face of the user. Our goal is to recognize two classes of gestures: deictic and symbolic.
ISBN:0769505805
9780769505800
DOI:10.1109/AFGR.2000.840674