Abstract
In this paper, we add the depth information to effectively locate the 3D position of the hands in the sign language recognition system. But, the information will be changed by the different testers and we can’t do the recognition well. So, we use the incremental changes of the three-dimensional coordinates on a unit time as the feature parameter to fix the above problem. We record the changes of the three-dimensional coordinates in time, then using the hidden Markov models to recognize the variety of sign language movement changing on the time domain. Experiment verifies the proposed method is superior to traditional ones.
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Wu, YK., Wang, HC., Chang, LC., Li, KC. (2013). Using HMMs and Depth Information for Signer-Independent Sign Language Recognition. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_8
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DOI: https://doi.org/10.1007/978-3-642-44949-9_8
Publisher Name: Springer, Berlin, Heidelberg
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