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Autoencoding Convolutional Representations for Real-Time Eye-Gaze Detection

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Computational Intelligence: Theories, Applications and Future Directions - Volume II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 799))

Abstract

Several real-life applications, like driver drowsiness detection systems, lie detector systems (eye accessing cues), are based on gaze direction of human eyes. Estimated gaze direction is an indication of user’s region of focus in space. In this work, we present a real-time eye-gaze detection pipeline on Eye Chimera database. Facial landmarks are used to detect face regions accurately, and we do provide a custom modification of Viola-Jones algorithm for eye region localization. The obtained eye regions dataset is fed to CNN models for training. Further, the obtained CNN features from the trained models have been fused using an autoencoder. This idea is not much explored in the literature which was much recommended in the AlexNet paper by Krizhevsky et al. 2012. Results demonstrate statistically significant (\(p<0.01\)) improved classification performance than the recently proposed methods on this database.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, Uttar Pradesh, India

    Tharun Kumar Reddy & Laxmidhar Behera

  2. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, 208016, Uttar Pradesh, India

    Vinay Gupta

Authors
  1. Tharun Kumar Reddy
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  2. Vinay Gupta
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  3. Laxmidhar Behera
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Corresponding author

Correspondence to Tharun Kumar Reddy .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Reddy, T.K., Gupta, V., Behera, L. (2019). Autoencoding Convolutional Representations for Real-Time Eye-Gaze Detection. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_18

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