Saad M. Darwish
ABSTRACT
Sign language recognition (SLR) has spawned more and more interest in human--computer interaction (HCI) society. The major challenge that faces SLR recognition now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape information that makes the accurate parameters of the HMM incapable of describing the uncertain distributions of the observations in gesture's features. This paper presents an extension of the HMMs using interval type-2 fuzzy sets to produce interval type-2 fuzzy HMMs to model uncertainties of hypothesis spaces. The advantage of this extension is that it can handle both the randomness and fuzziness of traditional HMM mapping. This system aspires to be a solution to the scalability problem, i.e. has real potential for application on a large vocabulary. Furthermore, does not rely on the use of data gloves or other means as input devices, and operates in isolated signer-independent modes. Experimental results show that the type-2 FHMM has a comparable performance as that of the FHMM but is more robust to the gesture variation.
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