Real-Time Prediction of Car Driver’s Emotions using Facial Expression with a Convolutional Neural Network-based Intelligent System

doi:10.23940/ijpe.22.11.p4.791797

Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (11): 791-797.doi: 10.23940/ijpe.22.11.p4.791797

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Real-Time Prediction of Car Driver’s Emotions using Facial Expression with a Convolutional Neural Network-based Intelligent System

Pawan Wawage^* and Yogesh Deshpande

Department of Computer Engineering, Vishwakarma University, Pune, 411048, India

Contact: *E-mail address: pawan.wawage-026@vupune.ac.in
About author:Pawan Wawage completed his Masters in Computer Science and Engineering at MIT, Pune, India. He is an Assistant Professor at the Department of Information Technology, and Research Scholar at Vishwakarma University. His research interests include issues related to Human-Computer Interaction, Cognitive Psychology, and Usability Engineering.
Yogesh Deshpande received his PhD in Human-Computer Interaction at IIT, Guwahati, India. He is a Professor at the Department of Computer Engineering, Vishwakarma University. His research interests include issues related to Human-Computer Interaction, Cognitive Psychology, and Industry 4.0. He is author of a great deal of research studies published at national and international journals, conference proceedings as well as book chapters.

Abstract

Abstract: When driving, the most crucial factor to consider is you and your passengers’ safety. Drivers must be kept under observation for any potential harmful act, whether intentional or inadvertent, in order to ensure a safe navigation. As a result, a real-time emotion detection system for a driver has been developed to detect, exploit, and evaluate the driver's emotional state. This paper discusses how to recognize emotions using facial expressions for application in active security systems for drivers. We discuss our research and development of a Convolutional Neural Network-based intelligent system for face image-based expression classification in this paper.

Key words: driver behavior, human-vehicle interaction, convolution neural network, facial expressions, deep neural networks

Pawan Wawage and Yogesh Deshpande. Real-Time Prediction of Car Driver’s Emotions using Facial Expression with a Convolutional Neural Network-based Intelligent System [J]. Int J Performability Eng, 2022, 18(11): 791-797.

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References

1. Lucey P., Cohn J. F., Kanade T., Saragih J., Ambadar Z., Matthews, I. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In2010 ieee computer society conference on computer vision and pattern recognition-workshops, IEEE, pp. 94-101, 2010
2. FER-2013.https://www.kaggle.com/c/challengesin-representation-learningfacial-expression-recognitionchallenge/data. (accessed on 21 November 2018)
3. KDEF. https://www.emotionlab.se/resources/kdef. (accessed on 27 November 2017)
4. Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
5. Harandi M. T., Sanderson C., Shirazi S., Lovell B. C. Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching. In CVPR2011. IEEE, 2 pp. 2705-271, 2011.
6. VIOLA AND JONES, C. Rapid object detection using a boosted cascade of simple features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA. 2001.
7. Vora S., Rangesh A., Trivedi M. M. On generalizing driver gaze zone estimation using convolutional neural networks. In2017 IEEE Intelligent Vehicles Symposium (IV). IEEE. pp. 849-854, 2017
8. Pantic M., Valstar M., Rademaker R., Maat, L. Web-based database for facial expression analysis. In2005 IEEE international conference on multimedia and Expo. IEEE, pp. 5, 2005
9. Izard C. E.Emotion theory and research: Highlights, unanswered questions, and emerging issues. Annual review of psychology, vol. 60, no. 1, 2009.
10. Eyben F., Wöllmer M., Poitschke T., Schuller B., Blaschke C., Färber B.,Nguyen-Thien, N. Emotion on the road—necessity, acceptance, and feasibility of affective computing in the car. Advances in human-computer interaction, 2010.
11. Krizhevsky, A., Sutskever, I. and Hinton, G.E.Imagenet classification with deep convolutional neural networks. Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

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Real-Time Prediction of Car Driver’s Emotions using Facial Expression with a Convolutional Neural Network-based Intelligent System

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