一种复杂光照下列车司机人脸自适应图像增强方法

An Adaptive Image Enhancement Method for Train Driver’s Face under Complex Illumination

针对列车高速行驶过程中,进入隧道后低光照和出隧道后的高光照图像,分别采取低光照和高光照图像增强方法进行处理,增强列车司机人脸图像阴暗区域,提出一种复杂光照下列车司机人脸自适应图像增强方法并进行了研究,实验结果表明,在复杂光照下列车司机人脸自适应图像增强方法能有效提高人脸检测成功率,降低误检率,为后续研究AdaBoost算法进行人脸精准检测,提取Haar特征以及积分图训练弱分类器和训练强分类器奠定一定基础。

In the process of high-speed train driving,the low illumination and high illumination images after entering and leaving the tunnel are processed by low illumination and high illumination image enhancement methods respectively to enhance the dark area of the train driver’s face image.An adaptive image enhancement method for train driver’s face under complex illumination is proposed and studied.The experimental results show that the adaptive image enhancement method for train driver’s face under complex illumination can effectively improve the face detection success rate and reduce th e false detection rate,which lays a foundation for the follow-up study of AdaBoost algorithm for accurate face detection,Haar feature extraction and integral image training of weak classifier and strong classifier.