Towards subject independent continuous sign language recognition: A segment and merge approach

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dc.contributor.author Kong, W. W.
dc.contributor.author Ranganath, Surendra
dc.date.accessioned 2014-03-17T09:38:56Z
dc.date.available 2014-03-17T09:38:56Z
dc.date.issued 2014-03
dc.identifier.citation Kong, W. W. and Ranganath, Surendra, “Towards subject independent continuous sign language recognition: A segment and merge approach”, Pattern Recognition, DOI: 10.1016/j.patcog.2013.09.014, vol. 47, no. 3, Mar. 2014. en_US
dc.identifier.issn 0031-3203
dc.identifier.uri http://dx.doi.org/10.1016/j.patcog.2013.09.014
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/847
dc.description.abstract This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers. en_US
dc.description.statementofresponsibility by W.W. Konga and Surendra Ranganath
dc.format.extent Vol 47, No. 3, pp 1294–1308
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Bayesian network en_US
dc.subject Gesture recognition en_US
dc.subject Semi markov CRF en_US
dc.subject Sign language recognition en_US
dc.subject Signer independence en_US
dc.subject Hidden Markov model
dc.subject Support vector machine
dc.title Towards subject independent continuous sign language recognition: A segment and merge approach en_US
dc.type Article en_US
dc.relation.journal Pattern Recognition


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