改进的 AdaBoost人脸检测方法

针对传统 AdaBoost算法检测速度快准确率低的问题，本文提出了一种改进的 AdaBoost算法以提高人脸的正确检测率，该算法首先利用快速积分图提取人脸的 Haar特征，然后使用阈值设定的方法对传统的 AdaBoost算法进行改进，并将每次检测的最优弱分类器级联形成最终的强分类器，通过强弱分类器对 Haar特征判别，从而检测图像中的人脸部分。采用本方法对多种实验图像集进行人脸检测实验， FERET彩色图像库的正确检测率为96.07%，视频图像的正确检测率为 96%。实验结果表明，本文所设计的人脸检测算法能够对静态图像以及视频图像中的人脸进行有效检测，为人脸的正确识别打下了基础，该算法也为计算机视觉领域的研究提供一种有效方法。

Face Detection Based on Modified AdaBoost Algorithm

According to the traditional AdaBoost algorithm with fast detection but low accuracy, a modified AdaBoost algorithm was presented to enhance the accuracy. First, the algorithm extracted Haar features of human face by rapid integral image. On the basis of this, it set the threshold value to modify the traditional AdaBoost algorithm and found the optimal weak classifier during each test, and then it cascaded them into strong classifier. Finally, strong classifier was developed to distinguish Haar feature and detect the part of face from images. The sample test results show that the classifier accuracy of FERET database is 96.07% and the video images is 96%. The experimental results demonstrate that the algorithm of human face detection designed can not only detect static images but also detect video images, which lay the foundation of face recognition and provide a kind of effective method for research of computer vision domain.