Hand gesture detection and extraction

A novel algorithm for detecting and extracting hand gestures is proposed which uses an on-line adaptive learning approach to fit the hand skin color distribution of each individual user in various environments. The on-line adaptive skin-color learning approach is designed by two strategies: negative...

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Bibliographic Details
Published in2013 IEEE China Summit and International Conference on Signal and Information Processing pp. 669 - 673
Main Authors Yea-Shuan Huang, Yu-Chung Chen, Fang-Hsuan Cheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2013
Subjects
Accuracy
Edge difference image
Hand detection
Hand tracking
High definition video
Image color analysis
Image edge detection
Lighting
Skin
Skin color learning
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Summary:A novel algorithm for detecting and extracting hand gestures is proposed which uses an on-line adaptive learning approach to fit the hand skin color distribution of each individual user in various environments. The on-line adaptive skin-color learning approach is designed by two strategies: negative skin-color exclusion and dynamic skin-color standard deviation. Negative skin-color exclusion can effectively remove the invalid skin-color pixels through a skin-color and non-skin color histogram discrimination. Dynamic skin-color standard deviation can derive the most appropriate range for skin-color judgment. Experimental results show the proposed method can detect and extract hand gestures more accurately than other methods.
DOI:10.1109/ChinaSIP.2013.6625426