Visual sign language recognition based on HMMs and auto-regressive HMMs

Xiaolin Yang*, Feng Jiang, Han Liu, Hongxun Yao, Wen Gao, Chunli Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.

Original languageEnglish (US)
Title of host publicationGesture in Human-Computer Interaction and Simulation
Subtitle of host publication6th International Gesture Workshop, GW 2005, Revised Selected Papers
PublisherSpringer Verlag
Number of pages4
ISBN (Print)3540326243, 9783540326243
StatePublished - 2006
Event6th International Gesture Workshop, GW 2005 - Berder Island, France
Duration: May 18 2005May 20 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3881 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Gesture Workshop, GW 2005
CityBerder Island


  • Autoregressive HMM
  • Computer Vision
  • HMM
  • Sign Language Recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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