Driver drowsiness is the major cause of many traffic accidents. A study by the National Institute of Sleep and Vigilance showed that the majority of the accidents took place on fast tracks. 34% of these accidents are mainly due to lack of sleep. Indeed, the drowsiness of drivers appears mainly by a fall of vigilance with the appearance of various behaviors that represent clues for the decrease of reflexes like the presence of yawning, heaviness of the eyelids and the difficulty to keep the head in frontal position compared to the vision field. In this context, we are interested particularly in the proposal of new solutions allowing the automatic control of the driver drowsiness states. These solutions need to be non-invasive while being based only on the analysis of the visual indices. These indices are calculated starting from the spatiotemporal information of the contents of a video stream. The main goal of our work is to recognize driver abnormal behavior by analyzing the characteristics of the face. In fact, we have proposed a fusion system based on detection of yawn, detection of somnolence and the 3D head pose estimation. This fusion system is evaluated by the three databases and shows many success of our suggested approach.