Earticle

초록

영어

It is easy for the internet learners to generate learning fatigue because of the long-term lack of emotional interaction in the learning process, and learning fatigue often manifests through the eye condition, in order to do effective monitoring for remote intelligent tutoring system, the learning fatigue eye state recognition algorithm is put forward based on Gabor wavelet and HMM. The algorithm has certain distinguishing characteristics aiming at the degree of eye openness of network learner under 3 learning states: normal learning, fatigue and confusion, first, it does gray difference disposal for eye image by Laplace operator in YCbCr color space, then, it selects two-dimension Gabor kernel function to build 48 optimal filters, obtain 48 characteristic values, these 48 characteristic values generate 48 eigenvectors, at last, it use a set of observation sequence O formed by eigenvector of HMM for eye state image to do eye state recognition. Experimental results show that the recognition rate of this algorithm for network learning reaches 95.68%, and this algorithm has a good robustness.

목차

Abstract
 1. Introduction
 2. Image Preprocessing
 3. Feature Extraction of Gabor Filter
 4. Eye State Recognition
  4.1. Basic Definition
  4.2. HMM Training
  4.3. HMM Eye Fatigue State Recognition
 5. Experimental Results and Analysis
 6. Conclusion
 Acknowledgement
 References

키워드

저자

• Yu Xiang [ Department of Information Technology, Tianjin Chengjian University, Tianjin City, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605