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Sign Energy Images for Recognition of Sign Language at Sentence Level

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Chethana Kumara B.M., Nagendraswamy H.S.

Chethana Kumara B.M. and Nagendraswamy H.S.. Article: Sign Energy Images for Recognition of Sign Language at Sentence Level. International Journal of Computer Applications 139(2):44-51, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Chethana Kumara B.M. and Nagendraswamy H.S.},
	title = {Article: Sign Energy Images for Recognition of Sign Language at Sentence Level},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {2},
	pages = {44-51},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In this paper, the task of sign language recognition at sentence level is addressed. The idea of Sign Energy Image (SEI) and a method of extracting Fuzzy-Gaussian Local Binary Pattern (FzGLBP) features from SEI to characterize the sign are explored. The suitability of interval valued type symbolic data for efficient representation of signs in the knowledgebase is studied. A Chi-square proximity measure is used to establish matching between reference and test signs. A simple nearest neighbor classification technique is used for recognizing signs. Extensive experiments are conducted to study the efficacy of the proposed system. A data base of signs called UoM-ISL is created for experimental analysis.


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Fuzzy Gaussian LBP, Interval valued features, Sign Energy Image, Sign language, Video sequence