A Boosted Classifier Tree for Hand Shape Detection

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A Boosted Classifier Tree for Hand Shape Detection
The ability to detect a persons unconstrained hand in a natural video sequence has applications in sign language, gesture recognition and HCI. This paper presents a novel, unsupervised approach to training an efficient and robust detector which is capable of not only detecting the presence of human hands within an image but classifying the hand shape. A database of images is first clustered using a k-mediod clustering algorithm with a distance metric based upon shape context. From this, a tree structure of boosted cascades is constructed. The head of the tree provides a general hand detector while the individual branches of the tree classify a valid shape as belong to one of the predetermined clusters exemplified by an indicative hand shape. Preliminary experiments carried out showed that the approach boasts a promising 99.8% success rate on hand detection and 97.4% success at classification. Although we demonstrate the approach within the domain of hand shape it is equally applicable...
Eng-Jon Ong, Richard Bowden
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where FGR
Authors Eng-Jon Ong, Richard Bowden
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