Facial expressions in American sign language: Tracking and recognition

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dc.contributor.author Nguyena, Tan Dat
dc.contributor.author Ranganath, Surendra
dc.date.accessioned 2014-03-16T13:26:50Z
dc.date.available 2014-03-16T13:26:50Z
dc.date.issued 2012-05
dc.identifier.citation Nguyen, T. D. and Ranganath, Surendra, “Facial expressions in American sign language: Tracking and recognition”, Pattern Recognition, DOI: 10.1016/j.patcog.2011.10.026, vol. 45, no.2, pp. 1877-1891, May, 2012. en_US
dc.identifier.issn 0031-3203
dc.identifier.uri http://dx.doi.org/10.1016/j.patcog.2011.10.026
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/796
dc.description.abstract This paper presents work towards recognizing facial expressions that are used in sign language communication. Facial features are tracked to effectively capture temporal visual cues on the signers' face during signing. Face shape constraints are used for robust tracking within a Bayesian framework. The constraints are specified through a set of face shape subspaces learned by Probabilistic Principal Component Analysis (PPCA). An update scheme is also used to adapt to persons with different face shapes. Two tracking algorithms are presented, which differ in the way the face shape constraints are enforced. The results show that the proposed trackers can track facial features with large head motions, substantial facial deformations, and temporary facial occlusions by hand. The tracked results are input to a recognition system comprising Hidden Markov Models (HMM) and a support vector machine (SVM) to recognize six isolated facial expressions representing grammatical markers in American sign language (ASL). Tracking error of less than four pixels (on 640×480 videos) was obtained with probability greater than 90%; in comparison the KLT tracker yielded this accuracy with 76% probability. Recognition accuracy obtained for ASL facial expressions was 91.76% in person dependent tests and 87.71% in person independent tests. en_US
dc.description.statementofresponsibility by Tan Dat Nguyena and Surendra Ranganath
dc.format.extent Vol. 45, No2, pp. 1877-1891
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Facial feature tracking en_US
dc.subject Facial expression recognition en_US
dc.subject Facial expression recognition en_US
dc.subject KLT tracker en_US
dc.subject American sign language
dc.subject Hidden markov models
dc.subject Probabilistic principal component analysis
dc.subject Support vector machine
dc.title Facial expressions in American sign language: Tracking and recognition en_US
dc.type Article en_US
dc.relation.journal Pattern recognition

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