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Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling

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Abstract

Eye-gaze tracking through camera is commonly used in a number of areas, such as computer user interface systems, sports science, psychology, and biometrics. The robustness of the head and camera rotation tracking algorithm has been a critical problem in recent years. In this paper, Haar-like features and a modified version of the group method of data handling, as well as segmented regression, are used together to find the base points of the eyes in a facial image. Then, a geometric transformation is applied to detect precise eye-gaze direction. The proposed algorithm is tested on GI4E and Columbia Gaze datasets and compared to other algorithms. The results show adequate accuracy, especially when the head/camera is rotated.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Saskatchewan, Saskatoon, SK, Canada

    Mohammad Reza Mohebbian

  2. Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan, Iran

    Javad Rasti

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  1. Mohammad Reza Mohebbian
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  2. Javad Rasti
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Mohebbian, M.R., Rasti, J. Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling. Machine Vision and Applications 31, 59 (2020). https://doi.org/10.1007/s00138-020-01112-2

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