近年来，由于驾驶员疲劳驾驶导致的交通事故逐年递增，所以有必要规范驾驶员的行为．由于驾驶员的疲劳状态可由眼睛状态表达出来，为了对眼睛状态进行有效监测，介绍了一种在车辆上安装CCD监测驾驶员行为的新方法，并介绍了一种采用计算机视觉对驾驶员的眼睛状态进行识别的技术方法．该方法是根据驾驶员在正常驾驶、瞌睡驾驶及疲劳驾驶3种状态下的眼睛张开程度有一定的区别的这一特点，提出的一种利用Gabor小波提取眼角处的纹理走向特征值，并将由所有特征值组成的特征矢量作为三层神经网络的输入，以输出对应3种不同精神状态的眼部状态的识别方法．试验结果表明，该网络可快速有效地识别出驾驶员眼部状态．

This paper describes a new method of the driver behavior surveillance by using a camera mounted on the test car. Recently, one of the important issues to heavy traffic accidence in the world is that many drivers steer tiredly or are absent-minded when steering car. This situation usually decreases the attention of drivers. Hence, traffic accidence occurs. Thus it is very important to surveille the drivers' behavior, especially the eyes' state recognition to drivers. As a matter of fact, investigation statistics indicates the degree of the drivers eyes' opening is different to some extent on the various occasions of the driver normal driving, the drowsing driving and the fatigued driving. So when the car runs, we obtain two-dimensional image through a on-board camera, from which we can get the eyes' state data. Based on this point, we have developed an effective algorithm to count the frequency of blinkling. This algorithm uses Gabor wavelet to pick up the texture character of the canthus. And the eigenvector that composed of eigenvlaue is the input of the ANN. We also have performed outdoor experiments to make sure that our algorithm works effective.