Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, X., Jiang, F., Liu, H., Yao, H., Gao, W., Wang, C. (2006). Visual Sign Language Recognition Based on HMMs and Auto-regressive HMMs. In: Gibet, S., Courty, N., Kamp, JF. (eds) Gesture in Human-Computer Interaction and Simulation. GW 2005. Lecture Notes in Computer Science(), vol 3881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11678816_9
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DOI: https://doi.org/10.1007/11678816_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32624-3
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