Automatic detection of a driver’s complex mental states

Ma, Z., Mahmoud, M. , Robinson, P., Dias, E. and Skrypchuk, L. (2017) Automatic detection of a driver’s complex mental states. In: Gervasi, O., Murgante, B., Misra, S., Borruso, G., Torre, C. M., Rocha, A. M. A.C., Taniar, D., Apduhan, B. O., Stankova, E. and Cuzzocrea, A. (eds.) Computational Science and Its Applications – ICCSA 2017: 17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part III. Series: Lecture notes in computer science (10406). Springer: Cham, pp. 678-691. ISBN 9783319623979 (doi: 10.1007/978-3-319-62398-6_48)

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Automatic classification of drivers’ mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers’ mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset’s extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Mahmoud, Dr Marwa
Authors: Ma, Z., Mahmoud, M., Robinson, P., Dias, E., and Skrypchuk, L.
College/School:College of Science and Engineering > School of Computing Science
Published Online:14 July 2017

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