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Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Author

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  • Susrutha Babu Sukhavasi

    (Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA)

  • Suparshya Babu Sukhavasi

    (Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA)

  • Khaled Elleithy

    (Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA)

  • Ahmed El-Sayed

    (Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA)

  • Abdelrahman Elleithy

    (Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA)

Abstract

Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods.

Suggested Citation

  • Susrutha Babu Sukhavasi & Suparshya Babu Sukhavasi & Khaled Elleithy & Ahmed El-Sayed & Abdelrahman Elleithy, 2022. "Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2352-:d:752545
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    References listed on IDEAS

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    1. Neska Haouij & Jean-Michel Poggi & Raja Ghozi & Sylvie Sevestre-Ghalila & Mériem Jaïdane, 2019. "Random forest-based approach for physiological functional variable selection for driver’s stress level classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 157-185, March.
    2. Sayan Putatunda, 2019. "Machine Learning: An Introduction," Springer Proceedings in Business and Economics, in: Arnab Kumar Laha (ed.), Advances in Analytics and Applications, pages 3-11, Springer.
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