Eye movement data modeling using a genetic algorithm

  • Authors:

  • Yun Zhang;Hong Fu;Zhen Liang;Xiaoyu Zhao;Zheru Chi;Dagan Feng;Xinbo Zhao

  • Affiliations:

  • School of Computer Science, Northwestern Polytechnical University , Xi'an, Shaanxi, P. R. China;Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and School of Information Technolo ...;Key Lab of Contemporary Design & Integrated Manu. Tech., Northwestern Polytechnical University, Xi'an, Shaanxi, P.R. China

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

We present a computational model of human eye movements based on a genetic algorithm (GA). The model can generate elemental raw eye movement data in a four-second eye viewing window with a 25 Hz sampling rate. Based on the physiology and psychology characters of human vision system, the fitness function of the GA model is constructed by taking into consideration of five factors including the saliency map, short time memory, saccades distribution, Region of Interest (ROI) map, and a retina model. Our model can produce the scan path of a subject viewing an image, not just several fixations points or artificial ROI's as in the other models. We have also developed both subjective and objective methods to evaluate the model by comparing its behavior with the real eye movement data collected from an eye tracker. Tested on 18 (9 × 2) images from both an obvious-object image group and a non-obvious-object image group, the subjective evaluations shows very close scores between the scan paths generated by the GA model and those real scan paths; for the objective evaluation, experimental results show that the distance between GA's scan paths and human scan paths of the same image has no significant difference by a probability of 78.9% on average.