Eye Fatigue State Recognition of Gabor Wavelet Optimization HMM Algorithm

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.