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Sign Language Recognition using Sub-Units

Cooper, HM, Ong, EJ, Pugeault, N and Bowden, R (2012) Sign Language Recognition using Sub-Units Journal of Machine Learning Research, 13. pp. 2205-2231.

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This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Cooper, HM
Ong, EJ
Pugeault, N
Bowden, R
Date : 25 July 2012
Contributors :
ContributionNameEmailORCID, I, V
Uncontrolled Keywords : Sign Language Recognition
Additional Information : Copyright 2012 The Author(s) Published by Journal of Machine Learning Research
Depositing User : Symplectic Elements
Date Deposited : 17 Nov 2015 18:26
Last Modified : 31 Oct 2017 17:46

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