基于肤色分割与改进Adaboost算法的人脸检测

摘要

Face can't be detected effectively from low-resolution video surveillance image in some circumstance,by combining the skin color training and segmentation of BP neural network,a detection area of interest is formed,the improved Adaboost algorithm is used for face detection.The experimental results show that the proposed method improves face detection accuracy and reduces the false detection rate of video surveillance images.%针对低分辨率的视频监控图像在复杂环境中不能有效地将人脸检测出来,提出结合BP神经网络的肤色训练和肤色分割,形成一个检测兴趣区域,利用改进的Adaboost算法来完成人脸检测.实验结果表明,改进的方法提高了人脸检测的准确率,降低了视频监控图像的误检率.