Study On Face Fatigue Detecting Technology Based On Video Image

Fatigue driving may lead accident of battleplan and military vehicle to affect military logistic transportation. Fatigue may also lead negligence of the frontier defence duty to severe aftereffect. The fatigue detection technique which can detect the fatigue states and generates some warning alarms is considered as one of the most prospective applications of automatic facial expression recognition. Compared with the other detecting methods, the method based on machine-vision has many advantages such as: real-time response, veracity, non-contact, applicability, economy, which make it become the focus of current research.Aiming at round-the-clock face detection, this paper proposes a face detecting algorithm using AdaBoost method. At first, the pupils are located using the red-eye effect of eyes in infrared illumination. The pupils'location is used to eliminate a majority of background. Then using AdaBoost method detects and locates face in the remanent region. This method simplifies the operation of traditional AdaBoost face detecting algorithm, and boosts the speed of face detection, benefits subsequent procedure, and improves the efficiency of the entire algorithm.Aiming at the problem of the great influence of illumination and the low reliability, this paper proposes a fast fatigue detection algorithm based on AdaBoost. At first, the face is located in the infrared video image. Two sub-images which respectively contain mouth and eyes are segmented from face image. Then, the eyes'and mouth's features which are extracted from the sub-images by PCA are classified by corresponding classifiers which are trained by AdaBoost earlier. Finally, the algorithm determines the state of the face is whether fatigued or not according to the output of the OR operation between the outputs of the classifiers. Proved by experiment, this algorithm has not only high correct rate and fast speed but also a powerful ability to generally use and kind of robustness. And the response time of this algorithm could satisfy the real-time requirement.