人脸检测系统实现技术研究

论文总字数：35532字

摘 要

本文实现一个人脸检测系统，所实现的人脸检测系统基于 Viola-Jones 人脸检测框架。对于人脸检测系统，两个关键点分别是准确性和高效性。文中着重说明了实现该人脸检测系统所需的四个关键方法。第一，利用简单的Haar-like 矩形特征来描述人脸，并使用“积分图”这一图像表示方式来形象地显示，这大大提高了特征值计算的速度。第二，将多个强分类器组合，并建立一个多层级的级联结构分类器，这一结构可以提高检测系统的整体效率，同时还能降低误检率。第三，使用代价敏感学习方法中的Asymmetric AdaBoost 算法来进行样本的训练，并得到简单、高效的分类器，与此同时这一过程还完成了从数量巨大的特征集中选择出小部分关键特征的任务。第四，在级联结构的人脸分类器的基础上，构建一个人脸检测器，用来扫描目标图像，从而确定人脸所在的区域。文中最后列举了一些实验案例以及实验结果。最终，本文实现了人脸检测系统，可以运行在常用的计算机平台上。而且系统可完成基本的检测功能，并做出评价。不过系统的误检率以及高效性还有待进一步优化。

关键词： 人脸检测、代价敏感学习、Asymmetric AdaBoost 方法、级联结构

Face Detection Syste

Abstract

This paper implements a face detection system, which is based on the Viola-Jones face detection framework. For the face detection system, the accuracy and efficiency are two key points.There are four important methods used in face detection system. The first method is to describe a face by using some simple Haar-like rectangular features, and use the “Integral Image” to representation an image to improve the speed of evaluating the features. The second method is to combine classifiers in a “cascade” and build a cascade classifier with multiple stages, which can improve the efficiency of system and decrease the false positive rate. The third method is to training samples for obtain a simple and efficient classifier by using cost-sensitive learning method Asymmetric AdaBoost algorithm, and to select a small number of critical features from a very large set of features.The forth method is build a face detector by using the cascade classifier, to scan the target image and determine the face area. A set of experiments and the evaluation of result are listed in the end. The face detection system is implemented on conventional computer platform. The system can detect the face in an image, and give the evaluation of the detection system. However, the false positive rate and the efficiency of the system need to be optimized.

Key Words：face detection, cost-sensitive learning, Asymmetric AdaBoost, structure of the cascade

目 录

摘要...................................................................................II

Abstract..............................................................................III

目录 ..................................................................................IV

第一章 绪 论............................................................................1

1.1 引言...........................................................................1

1.2 选题背景.......................................................................1

1.3 论文的主要工作.................................................................2

1.4 论文组织结构...................................................................2

第二章 人脸检测方法概述.................................................................4

2.1 常用人脸检测方法...............................................................4

2.1.1 模板匹配 AntiFaces 方法..................................................4

2.1.2 支持向量机（SVM）........................................................4

2.1.3 贝叶斯决策特征法.........................................................4

2.1.4 神经网络.................................................................4

2.2 人脸检测方法分类...............................................................5

2.2.1 基于特征不变性的方法（feature invariant approaches）.....................5

2.2.2 基于经验知识的方法（knowledge-based methods）............................5

2.2.3 基于模版匹配的方法（template matching methods） .........................5

2.2.4 基于外观的方法（appearance-based methods） ...............................6

2.3 本章小结.......................................................................6

第三章 系统实现方法概述.................................................................7

3.1 训练级联结构分类器.............................................................7

3.1.1 积分图表示法.............................................................7

3.1.2 代价敏感学习算法.........................................................7

3.1.3 级联分类器结构...........................................................8

3.2 实现人脸检测器.................................................................8

3.3 检测器性能评价.................................................................8

3.4 本章小结.......................................................................9

第四章 Haar-like 矩形特征与积分图计算..................................................10

4.1 Haar-like 矩形特征的选取......................................................10

4.2 利用积分图计算特征值..........................................................11

4.3 本章小结......................................................................12

第五章 级联结构........................................................................13

5.1 级联结构说明..................................................................13

5.2 训练级联结构分类器............................................................14

5.3 本章小结......................................................................16

第六章 代价敏感学习算法................................................................17

6.1 代价敏感学习方法概述..........................................................17

6.2 Adaboost 训练算法.............................................................17

6.3 Asymmetric AdaBoost 算法 .....................................................19

6.4 分类器结构....................................................................21

6.4.1 弱分类器....................................................................21

6.4.2 强分类器....................................................................21

6.5 本章小结......................................................................22

第七章 检测器的实现....................................................................23

7.1 多尺寸扫描子窗口..............................................................23

7.2 子窗口中特征值计算............................................................24

7.3 合并多个检测窗口..............................................................24

7.4 本章小结......................................................................25

第八章 实验结果与性能评测..............................................................26

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