摘要人脸作为人体最具特征性的一部分，在智能检测中占据着重要的位置。人脸检测是指在复杂的环境下，通过图像或视频采集数据，利用智能数据处理系统，检测出人脸的位置与大小的一种技术。本文着重介绍复杂背景下人脸检测技术，因为人脸检测技术不仅是表情识别，脸部特征识别，人脸跟踪的首要前提，也在安全视频监控，人机交互，医疗监控等领域起到了重要作用。
本文从基于知识，特征不变量，模板匹配，基于外观四种基础检测方法出发，介绍了特征脸，神经网络，隐马尔科夫模型，向量机等经典方法，着重阐述了Adaboost算法并用矩形特征和积分图完成分类器的选择，并在此理论基础上，通过VC++ 2008在基于Opencv平台上对人脸进行检测仿真，综合检测率接近90%,实现了人脸的检测。19925
关键词  人脸检测  Adaboost算法  Opencv   复杂背景
毕业设计说明书（论文）外文摘要
Title    Face Detection Under Complex Environment              
Abstract
As part of the human face of the most characteristic, the intelligent detection occupies an important position. Face detection is a technique to detect the position and size of the face in a complex environment, image or video capture data by using intelligent data processing system. Face tracking will not be described in detail here. This article focuses on complex backgrounds Human Face Detection Technology, Face Detection technology is not only the most important prerequisite in expression recognition, facial feature recognition and face tracking ，but also played an important effect on security video surveillance, human-computer interaction, medical monitoring and other fields.
From the knowledge-based, feature invariant, template matching, detection method based on the appearance of four basic,I introduce characteristic face, neural networks, hidden Markov model, vector machines and other classical methods, Focuses on the Adaboost algorithm and rectangular features and integral image to complete classifier selection, and on this theoretical basis,we use VC + + 2008 based on Opencv platform for face detection simulation, synthesis detection rate of nearly 90 percent, achieved a face detection.
Keywords  Face detection  Adaboost algorithm  OpenCV  Complex background
目   次
1  绪论    1
1．1  背景    1
1.2  人脸检测的研究现状    2
1.3  人脸检测存在的问题    2
1.4  人脸检测结果的评价标准    3
2  检测方法    4
2.1  基于知识的方法    4
2.2  基于特征不变量的方法    5
2.3  模板匹配方法    5
2.4  基于表象的方法    5
2.5  特征脸    5
2.6  神经网络    6
2.7  隐马尔科夫模型（HMM）    6
2.8  支持矢量机（SVM）    6
3  AdaBoost 算法    7
3.1  AdaBoost 简介    7
3.2  AdaBoost 人脸检测    7
3.3  弱学习和强学习    8
3.4  Adaboost 算法性能    8
4  矩形特征和积分图    10
4.1  矩形特征    10
4.2  积分图    11
4.3  Haar特征值计算    13
5  OpenCV的程序实现    15
5.1  OpenCV简介    15
5.2  OpenCV的特征    16
5.3  实现程序    16 Adaboost+Opencv复杂背景下人脸检测技术研究:http://www.youerw.com/tongxin/lunwen_11505.html