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Large Lexicon Detection Of Sign Language

Cooper, H and Bowden, R (2007) Large Lexicon Detection Of Sign Language

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This paper presents an approach to large lexicon sign recog- nition that does not require tracking. This overcomes the issues of how to accurately track the hands through self occlusion in unconstrained video, instead opting to take a detection strategy, where patterns of motion are identi ed. It is demonstrated that detection can be achieved with only minor loss of accuracy compared to a perfectly tracked sequence using coloured gloves. The approach uses two levels of classi cation. In the rst, a set of viseme classi ers detects the presence of sub-Sign units of activity. The second level then assembles visemes into word level Sign using Markov chains. The system is able to cope with a large lexicon and is more expandable than traditional word level approaches. Using as few as 5 training examples the proposed system has classi cation rates as high as 74.3% on a randomly selected 164 sign vocabulary performing at a comparable level to other tracking based systems.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors : Cooper, H and Bowden, R
Date : 2007
DOI : 10.1007/978-3-540-75773-3_10
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 23 Jan 2020 12:46

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