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Facial expressions (FE) are one of the important cognitive load markers in the context of car driving. Any muscular activity can be coded as an action unit (AU) which are the building blocks of FE. Precise facial point tracking is crucial since it is a necessary step for AU detection. Here, we present our progress in FE analysis based on AU detection on face infrared videos in the context of a car driving simulator. First, we propose a real-time facial points tracking method (HCPF-AAM) using a modified particle filter (PF) based on Harris corner samples which is optimized and combined with an Active Appearance Model (AAM) approach. Robustness of PF, precision of Harris corner-based samples, and optimization of AAM result in a powerful facial points tracking on very low-contrast images acquired under near-infrared (NIR) illumination. Second, detection of the most common AUs in the context of car driving, identified by a certified Facial Action Coding System coder is presented. For detection of each specified AU, the spatio-temporal analysis of related tracked facial points is performed. Then, a combination of rule-based scheme with Probabilistic Actively Learned Support Vector Machines is developed to classify the features calculated from the related tracked facial points. Results show that with such a scheme, we can obtain more than 91% of precision in the detection of the five most common AUs for low-contrast NIR images and 90% of precision in the MMI dataset.

Infrared-based facial points tracking and action units detection in context of car driving simulator