【作者】 韩春霞；

【导师】 苑玮琦；

【作者基本信息】 沈阳工业大学 ， 测试计量技术及仪器， 2009， 硕士

【摘要】 人脸定位(face detection)是指在输入图像中确定所有人脸(如果存在)大小、位置、位姿的过程。人脸定位作为人脸信息处理中的一项关键技术,近年来成为模式识别与计算机视觉领域内一项受到普遍重视、研究十分活跃的课题。近年来人脸定位技术的研究已经取得了长足的进展,涌现出了许多新的人脸定位方法。目前人脸定位的主要方法分为四类:基于知识的方法、基于特征的方法、基于模板匹配的方法、基于外观学习的方法。在众多的人脸定位算法中,2001年Paul Viola和Michael Jones提出的基于AdaBoost算法的人脸定位方法从根本上解决了人脸定位的速度问题,同时具有较好的检测效果。肤色是人脸的重要信息,不依赖面部的细节特征,对于旋转、表情等变化情况都能适用,具有相对的稳定性并且和大多数背景物体的颜色相区别,但是当背景复杂的情况下可能存在大量的类肤色物体时,导致了该算法较高的误检率。基于Adaboost的人脸定位其基本思想是当分类器对某一样本分类错误时,增加其权重;当分类正确时,则减少该样本的权重,使得后续的分类器更加强化对分类错误的样本进行训练,最终得到一个十分理想的分类器。该方法从根本上解决了人脸定位的速度问题,而且具有较低的误检率,但是该方法的检测率不如肤色检测的检测率高。基于上述原因,本文采用肤色分割与AdaBoost相结合的方法进行人脸定位,在提高检测率的同时降低了误检率。本文首先,输入待检测图像,将其转换至YCbCr色彩空间,根据肤色的聚集范围将其二值化,并经过形态学预处理得到人脸的候选区域。在预处理过程中,采用可调节结构元素,解决了对于不同图像中人脸大小不一采用固定的结构元素造成的人脸丢失问题,提高了检测率。然后,将肤色分割后的人脸候选区域做为AdaBoost的输入窗口进行人脸定位。最后,在待检测图像中标记所得到的人脸区域。更多还原

【Abstract】 Face detection (face detection) is the process that determine all of the face (if it exists) the size, location, position and orientation from input image. Face detection is a key technology of human face information processing, it becomes a attracted universal attention, very active research topic in the field of pattern recognition and computer vision. The research in face detection technology in recent years, has made considerable progress, there have been many new face detection method. Currently the primary method of face detection is divided into four categories: the method based on knowledge, the method based on characteristics, the method based on template matching, the method based on appearance and learning. Among the human face detection algorithm, Paul Viola and Michael Jones proposed algorithm based on AdaBoost face detection method in 2001. The method solved the speed of face detection problem in fundamentally, ane has good detection results.Skin color is the important information of the human face, and it does not rely on the details of facial features, and the rotation, expression changing can be applied. It has relative stable and distinguish with most of the background objects.But the algorithm has led to a high false positive rate when the background is very complicated , there may be a large number of class color object case. The basic idea of based on AdaBoost face detection is that when the classifier to a sample of classification error, increase its weight; When the classification accuracy, then reduce the weight of the sample, making the classifier more intensive follow-up of the classification error of the sample training and eventually get a very good classifier. This method is a fundamental solution to the speed of face detection, but also has a lower false detection rate, end the method of detection rate is lower than skin color detection rate.For above these reasons, in this paper, skin color segmentation method and combination of AdaBoost face detection was used. It improves the detection rate while reducing the false detection rate. In this paper, input the detecting image and convert it into YCbCr color space, make it to binary image according to the aggregation range of color values, and get candidate face region after morphological preprocessing. In the pretreatment process, the using adjusted structural elements can solve human face loss problem bacause of the human face images of different sizes in different image uesd the stable structural elements and improved the detection rate. Then, after the face color segmentation candidate region as the input windows of AdaBoost face detection. Finally, in the detection of an image tag to be received by the face region.更多还原