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Gaze direction estimation using support vector machine with active appearance model

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Abstract

In recent years, research on human-computer interaction is becoming popular, most of which uses body movements, gestures or eye gaze direction. Until now, gazing estimation is still an active research domain. We propose an efficient method to solve the problem of the eye gaze point. We first locate the eye region by modifying the characteristics of the Active Appearance Model (AAM). Then by employing the Support Vector Machine (SVM), we estimate the five gazing directions through classification. The original 68 facial feature points in AAM are modified into 36 eye feature points. According to the two-dimensional coordinates of feature points, we classify different directions of eye gazing. The modified 36 feature points describe the contour of eyes, iris size, iris location, and the position of pupils. In addition, the resolution of cameras does not affect our method to determine the direction of line of sight accurately. The final results show the independence of classifications, less classification errors, and more accurate estimation of the gazing directions.

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Acknowledgment

This work was partially supported by the National Science Council, Taiwan, under the Grants No. NSC101-2221-E-011-141, NSC100-2221-E-011-121, and NSC101-2221-E-211-011.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Yi-Leh Wu, Chun-Tsai Yeh & Wei-Chih Hung

  2. Department of Information Management, Huafan University, Taipei, Taiwan

    Cheng-Yuan Tang

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  1. Yi-Leh Wu
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  2. Chun-Tsai Yeh
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  3. Wei-Chih Hung
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  4. Cheng-Yuan Tang
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Correspondence to Yi-Leh Wu.

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Wu, YL., Yeh, CT., Hung, WC. et al. Gaze direction estimation using support vector machine with active appearance model. Multimed Tools Appl 70, 2037–2062 (2014). https://doi.org/10.1007/s11042-012-1220-z

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