Considering that achieving non-contact and high precision in fatigue driving detection is difficult

the method based on computer vision arises as a possible solution. The key point to achieve fatigue driving detection is to recognize the open and closed state of driver's eyes and estimate if the driver is fatigued and sleepy. This paper proposes a new fatigue driving detection method based on sclera segmentation to solve the problem of recognizing the open and closed state of driver's eyes in fatigue driving. The face is tested from the acquired picture using a face detection method based on Adaboost raised by Viola and Jones. Then

a Gauss sclera model based on YCbCr color space is built for the next step because of the good clustering performance of sclera in Cb-Cr color space. The sclera is detected in the face region using this model and the area of the sclera is calculated. The sclera area is considered the eye opening index and the fatigue state is determined along with the PERCLOS criterion. Experimental results for 10 short testing videos show that the proposed algorithm can select the sclera part and recognize the open and closed state of human eyes effectively. The accuracy rate can reach up to 96.77%. The proposed method can achieve satisfactory segmentation effect under good lighting condition. Using the sclera area as the eye opening index

the proposed method can also judge the state of driver's fatigue accurately. The new approach is effective and must be further studied.