Title :
Adaptive Sign Language Recognition With Exemplar Extraction and MAP/IVFS
Author :
Zhou, Yu ; Chen, Xilin ; Zhao, Debin ; Yao, Hongxun ; Gao, Wen
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
fDate :
3/1/2010 12:00:00 AM
Abstract :
Sign language recognition systems suffer from the problem of signer dependence. In this letter, we propose a novel method that adapts the original model set to a specific signer with his/her small amount of training data. First, affinity propagation is used to extract the exemplars of signer independent hidden Markov models; then the adaptive training vocabulary can be automatically formed. Based on the collected sign gestures of the new vocabulary, the combination of maximum a posteriori and iterative vector field smoothing is utilized to generate signer-adapted models. Experimental results on six signers demonstrate that the proposed method can reduce the amount of the adaptation data and still can achieve high recognition performance.
Keywords :
adaptive systems; feature extraction; gesture recognition; hidden Markov models; iterative methods; smoothing methods; vectors; MAP/IVFS; adaptive sign language recognition; adaptive training vocabulary; affinity propagation; exemplar extraction; hidden Markov models; iterative vector field smoothing; Affinity propagation; maximum a posteriori; sign language recognition; signer adaptation; vector field smoothing;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2009.2038251