a robust and accurate particle filter-based pupil detection method for big datasets of eye video

Accurate detection of pupil position in successive frames of eye videos is finding applications in many areas including assistive systems and e-learning. processing the big datasets of eye videos in such systems requires robust and fast eye-tracking algorithms that can predict the position of eye pupil in consecutive video frames. as a major technique, particle filters provide adequate speed but have a low detection rate. to solve this problem, the present paper suggests the use of genetic algorithms in the sampling step of the particle filter technique. as a result, in each frame, the variety of particles required for predicting the pupil position in the next video frame is maintained and their uniformity is reduced. finally, the speed and detection rate of the proposed method, as well as the basic particle filter method in predicting the pupil position in video frames are calculated and compared for various populations. the experimental results indicate that, in comparison with the basic particle filter algorithm, the proposed algorithm detects the pupil more accurately and in a shorter time. also, by achieving an average detection rate of 79.89% in estimation of the pupil center with an error of five pixels on a variety of eye videos with different situations of occlusion and illumination, not only the robustness of the proposed method is assessed but also its superiority to the state-of-the-art methods is evinced.