| Safety is always one of the eternal topics in vehicle Transportation. With the rapid development of transportation, traffic accidents are greatly increasing, especially including traffic fatality. Safety problem in transportation is paid more and more attention worldwide. Under this circumstance, Safety Driving Assist technologies, as a part of Intelligent Vehicle technologies, are paid more and more attention and it can support greatly for reducing the road accidents due to drivers' human factors. The developed countries, such as the U.S., U.K., Japan, Germany etc., have made their research programs to develop Safety Driving Assist technologies and have already achieved great progress. Some systems about Safety Driving Assist technologies have been applied in the passenger car, heavy truck, bus and public transport and special vehicle.Driver's human factors have been one of the most important causes of road accidents. Driver monitoring has been a focus of Safety Driving Assist technologies research. The Driver monitoring method of Machine vision has better advantage than other method on time, accuracy, adaptability and economical respects. Therefore, most of researchers have studied the driver's monitoring method based on machine vision by vehicle-mounted cameras. Until now many research have focused on monitoring the driver's face, eye, pupil and so on to obtain his/her face rotation and orientation, eye activities, eye blinking rate, gaze direction, finally to determine his/her fatigue or distraction state. However, Most of researchers have neglected driver's fatigue state such as driver's yawning and his/her distraction like conservation and talking on a cellular phone while his/her driving. This paper presents a method for real-time monitoring a driver's mouth state by one vehicle-mounted camera, and monitoring driver's fatigue state such as driver's yawning and his/her distraction like conservation and talking on a cellular phone while his/her driving by recognizing his/her mouth state based on machine vision first in our country, extends the Safety Driving Assist technologies, and provides the reference and support for the integrated driver monitoring technologies. Obviously, driver's fatigue and distraction warning system take important role on reducing accident rate.The research work in this paper include the four parts, i.e. driver's face detection, driver's mouth detection and tracking, driver's mouth state recognition and driver's fatigue and distraction state identification.Driver's face detection applies the human face skin color model. The color distribution of skin colors of different people was found to be clustered in a small area of the chromatic YCrCb color space. Although skin colors of different people appear to vary over a wide range, they differ much less in Cr,Cb chroma than in brightness. In other words, skin colors of different people are very close, but they differ mainly in intensities. Results showed that skin color Cr,Cb chroma distribution of different people can be represented by a Gaussian model. This paper proposed a fast, available face detection method under different background using the human skin color property in YCrCb color space. Experiment results showed that this method is much reliable and adaptable to driver's different pose. Driver's mouth detection starts with the driver's lip pixels segmentation. Lip pixels are prevalently redder than skin pixels, not pure red. And normalized RGB is invariant to changes of the light source, face orientation and rotation. In normalized RGB color space Fisher transformation is used to determine an optimal projection vector , onto which rgb color vector data of lip and skin pixels are distinguished. This method segments lip and skin pixels, keeps the distinct lip contour boundary and increases lip detection accuracy. This paper locates driver's mouth by connected component analysis and region geometric constraint. Kalman filter is used to track Driver's mouth.Connected component analysis algorith...
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