摘要
人脸是人体的一项重要的生物特征，人脸检测在人脸识别中起着非常重要的作用，人脸位置的检测结果的准确性可以有效地提高人脸识别率；人脸定位在身份验证、人机交互、视频监控、机器学习、信息管理等领域有很高的应用价值．笔者提出一种人脸检测定位算法，对图像进行归一化和均衡化，减小检测范围，然后用Haar—like矩形特征形成弱分类器，结合AdaBoost学习算法将多个弱分类器组合成强分类器，对人脸图像进行检测定位．实验结果证明该方法可以有效地降低误检率，提高检测的准确性．
The face is one of the most important biological characteristics of the human body. Face detection plays a key role in face recognition, and the accuracy of detecting results can effectively improve the correct rate of face recognition. In authentication, human - computer interaction, video monitoring, machine learning, information management and other fields, face detection has very important application. This paper propose a face detection algorithm. We apply image normalization and equalization first, which can reduce the detection range. And then we use the Haar - like rectangle feature to form multiple weak classifiers. At last, combine these weak classifiers with the AdaBoost learning algorithm form a strong classifier, which is used in the face detection of image. The experimental results show that this algorithm can effectively reduce the false detection rate, improve the accuracy of face detection.
出处
《山东师范大学学报（自然科学版）》
CAS
2015年第4期34-37,共4页
Journal of Shandong Normal University(Natural Science)
