A boosted classifier tree for hand shape detection

• Published in: Automatic Face and Gesture Recognition: Proceedings, 6th International Conference, Seoul, Korea, 2004 pp. 889 - 894
• Main Authors: Eng-Jon Ong, Bowden, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Classification tree analysis; Clustering algorithms; Detectors; Handicapped aids; Humans; Image databases; Robustness; Shape; Tree data structures; Video sequences
• ISBN: 0769521223; 9780769521220
• DOI: 10.1109/AFGR.2004.1301646

The ability to detect a persons unconstrained hand in a natural video sequence has applications in sign language, gesture recognition and HCl. 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-method 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 to other problems where both detection and classification are required for objects that display high variability in appearance.