Studying facial features has attracted increasing attention in both academic and industrial communities. Indeed, these features convey nonverbal information that plays a key role in humancommunication. Moreover, they are very useful to allow human-machine interactions. Therefore, the automatic study of facial features is an important task for various applications includingrobotics, human-machine interfaces, behavioral science, clinical practice and monitoring driver state. In this thesis, we focus our attention on monitoring driver state through its facial features analysis. This problematic solicits a universal interest caused by the increasing number of road accidents, principally induced by deterioration in the driver vigilance level, known as hypovigilance. Indeed, we can distinguish three hypovigilance states. The first and most critical one is drowsiness, which is manifested by an inability to keep awake and it is characterized by microsleep intervals of 2-6 seconds. The second one is fatigue, which is defined by the increasing difficulty of maintaining a task and it is characterized by an important number of yawns. The third and last one is the inattention that occurs when the attention is diverted from the driving activity and it is characterized by maintaining the head pose in a non-frontal direction.The aim of this thesis is to propose facial features based approaches allowing to identify driver hypovigilance. The first approach was proposed to detect drowsiness by identifying microsleepintervals through eye state analysis. The second one was developed to identify fatigue by detecting yawning through mouth analysis. Since no public hypovigilance database is available,we have acquired and annotated our own database representing different subjects simulating hypovigilance under real lighting conditions to evaluate the performance of these two approaches. Next, we have developed two driver head pose estimation approaches to detect its inattention and also to determine its vigilance level even if the facial features (eyes and mouth) cannot be analyzed because of non-frontal head positions. We evaluated these two estimators on the public database Pointing'04. Then, we have acquired and annotated a driver head pose database to evaluate our estimators in real driving conditions.

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   |texte=   Extraction and analysis of facial features : application to drover hypovigilance detection
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