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http://dx.doi.org/10.5573/ieie.2015.52.9.077

Hierarchical Hidden Markov Model for Finger Language Recognition  

Kwon, Jae-Hong (Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Kim, Tae-Yong (Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.9, 2015 , pp. 77-85 More about this Journal
Abstract
The finger language is the part of the sign language, which is a language system that expresses vowels and consonants with hand gestures. Korean finger language has 31 gestures and each of them needs a lot of learning models for accurate recognition. If there exist mass learning models, it spends a lot of time to search. So a real-time awareness system concentrates on how to reduce search spaces. For solving these problems, this paper suggest a hierarchy HMM structure that reduces the exploration space effectively without decreasing recognition rate. The Korean finger language is divided into 3 categories according to the direction of a wrist, and a model can be searched within these categories. Pre-classification can discern a similar finger Korean language. And it makes a search space to be managed effectively. Therefore the proposed method can be applied on the real-time recognition system. Experimental results demonstrate that the proposed method can reduce the time about three times than general HMM recognition method.
Keywords
hand recognition; leap motion; finger language; hidden markov model; hierarchical model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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