New paper on Prediction of Cognitive Load by Anil Yüce et al.

© 2017 EPFL

© 2017 EPFL

The paper entitled "Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving" has been published in the IEEE Transactions on Affective Computing.

The paper is authored by Anil Yüce, Hua Gao, Gabriel Cuendet and Jean-Philippe Thiran. It has been published on June 23 in the IEEE Transactions on Affective Computing, Vol. 8, No 2, pp.161 - 175

The abstract is:

Driving requires the constant coordination of many body systems and full attention of the person. Cognitive distraction (subsidiary mental load) of the driver is an important factor that decreases attention and responsiveness, which may result in human error and accidents. In this paper, we present a study of facial expressions of such mental diversion of attention. First, we introduce a multi-camera database of 46 people recorded while driving a simulator in two conditions, baseline and induced cognitive load using a secondary task. Then, we present an automatic system to differentiate between the two conditions, where we use features extracted from Facial Action Unit (AU) values and their cross-correlations in order to exploit recurring synchronization and causality patterns. Both the recording and detection system are suitable for integration in a vehicle and a real-world application, e.g., an early warning system. We show that when the system is trained individually on each subject we achieve a mean accuracy and F-score of ∼95 percent, and for the subject independent tests ∼68 percent accuracy and ∼66 percent F-score, with person-specific normalization to handle subject dependency. Based on the results, we discuss the universality of the facial expressions of such states and possible real-world uses of the system.

This paper is one of the main results of our major collaboration with PSA Peugot-Citroën and Valéo.