SIGNER-INDEPENDENT SIGN LANGUAGE RECOGNITION BASED ON HMMs AND DEPTH INFORMATION

Abstract

In this paper, we use the depth information to effectively locate the 3D position of hands in sign language recognition system. But the information will be changed by different signers and we can’t do recognition well. Here, we use the incremental changes of the three- dimensional coordinates on a unit time as feature set to fix the above problem. And we use hidden Markov models(HMMs) as time-varying classifier to recognize the moving change of sign language on time domain. We also include HMMs with scaling factor to solve the underflow effect of HMMs. Experiments verify that the proposed method is superior then traditional one.中華民國影像處理與圖形識別學會; 宜蘭大學圖書資訊館; 宜蘭大學圖書資訊館國際20130818~20130820紙本Y宜蘭, 臺