This paper focuses on the effects of light condition, glass-wearing on driver’s eye, and proposes a way of human eye detection by Hough transform and neural network classifier. Firstly, two eye candidate regions were selected based on the geometry and symmetry of the iris. Then, coarse human eye positioning is conducted by edge detection and MAE operator. At last, the B-P neural network was utilized to pinpoint the human eye. For six groups of video images captured in three different situations, that is, different light, different backgrounds and different skin color, three groups were performed simulation experiment using matlab7.0. Results show that the algorithm for complex situations human eye detection has a strong robustness, improve the accuracy of eye detection greatly.%针对光照、眼镜等对驾驶员人眼检测的影响，提出采用霍夫变换和神经网络分类器进行人眼检测.通过应用虹膜几何信息和对称性，选择可能包含人眼的两个候选区域.运用边缘检测算子和 MAE 进行人眼粗定位.然后在此基础上采用B-P神经网络进行人眼精确定位.针对三种不同情况，即不同光照、不同背景和不同肤色的人拍摄6组视频图像，采用matlab7.0进行3组仿真实验，实验结果表明该算法对复杂情况的人眼检测具有较强的鲁棒性.大大提高人眼检测准确率.
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