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二 ○ 一 三 届 毕 业 设 计
基于 Andriod 移动设备嵌入式机器视觉的人脸识别
系统设计
学 院:
专 业:
姓 名:
学 号:
指导教师:
完成时间:2013 年 6 月 16 日
二〇一三年七月
毕业设计报告纸
摘 要
人脸识别是在图像或视频流中进行人脸的检测和定位,其中包括人脸在图像或视频流中
的所在位置、大小、形态、个数等信息,近年来由于计算机运算速度的飞速发展使得图像处
理技术在许多领域得到了广泛应用,其中包含智能监控、安全交易、更安全更友好的人机交
互等。如今在许多公司或研究所已经作为一门独立的课题来研究探索。
近年来,随着移动互联网的发展,智能手机平台获得了长足的发展。然而,手机钱包、
手机远程支付等新应用的出现使得手机平台的安全性亟待加强。传统的密码认证存在易丢失、
易被篡改等缺点,人脸识别不容易模仿、篡改和丢失,因而适用于手机安全领域中的应用。
本论文在分析国内外人脸识别研究成果的基础上,由摄像头采集得到人脸图像,在高性
能嵌入式系统平台上,采用 JAVA 高级语言进行编程,对检测得到的图像进行人脸检测、特
征定位、人脸归一化、特征提取和特征识别。在 Android 平台上实现了基于图像的人脸识别
功能。
本文主要的研究内容:首先对当前人脸识别技术的研究现状和常用的人脸检测和人脸识
别方法做了扼要的介绍,然后着重介绍了 Adaboost 人脸检测算法和通过 LBP 直方图匹配的
人脸识别算法,最后基于这两种人脸检测和人脸识别的算法,在 Android 平台上通过移植
OpenCV 并进行编程从而实现了移动设备的人脸识别功能。
关键词:Android ,OpenCV,人脸识别,Eclipse
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毕业设计报告纸
Abstract
The face recognition is to face detection and location in the image or video stream, including
the location of the face in the image or video stream, the size, shape, and then number of
information in recent years due to the rapid computing speed makes the development of image
processing technology has been widely applied in many fields, which includes intelligent
monitoring, secure transactions, safer and more friendly and human-computer interaction. Today, as
a separate subject many companies or research are to study and explore.
In recent years,smart phone platforms achieve rapid development according toprosperous of
3G wireless technology.The applications,like mobile payment,remote transaction,make our
life easier but bring more safety issues too.Traditional safety certification uses password as
authentication method.which is 1iable to falsification and forgetfulness.Facial feature Call
overcome the disadvantages brought by traditional methods,So it is fit for safety applications on
smart phone platform.
Based on the research results of the analysis of face recognition at home and abroad in this
paper, We obtained the facial images obtained by the camera and then used Senior JAVA language
to program for face detection, feature localization , face normalization, feature extraction and
pattern recognition in in high-performance embedded system platform. It implemented the face
recognition function based on images on the Android platform.
The research contents in this paper are as follows: first introduced the current status of the face
recognition technology and the common face detection and face recognition methods briefly, and
then focused on the Adaboost face detection algorithm and face recognition algorithm of matching
people through LBP histogram. At last, it enabled the face recognition function of mobile devices
by transplanting OpenCV and programing on the Android platform based on these two face
detection and face recognition algorithm.
KEYWORDS: Android,OpenCV, face recognition,Eclipse
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毕业设计报告纸
目 录
摘 要..............................................................................................................................................2
Abstract.........................................................................................................................................3
第一章 绪论..................................................................................................................................6
1.1 研究背景及意义.........................................................................................................................6
1.2 国内外研究现状.........................................................................................................................7
1.3 本文研究的主要内容.................................................................................................................9
1.4 论文结构安排..........................................................................................................................10
1.5 本章小结..................................................................................................................................10
第二章 人脸检测和识别的算法选择.........................................................................................11
2.1 人脸识别的研究内容...............................................................................................................11
2.2 人脸检测..................................................................................................................................11
2.2.1 基于知识的方法.................................................................................................13
2.2.2 特征不变量方法.................................................................................................14
2.2.3 模板匹配的方法.................................................................................................14
2.2.4 基于表象的方法.................................................................................................15
2.3 人脸识别..................................................................................................................................16
2.3.1 基于几何特征的识别方法..................................................................................16
2.3.2 基于特征脸的识别方法......................................................................................16
2.3.3 基于神经网络的方法..........................................................................................16
2.3.4 基于支持向量机的方法......................................................................................17
2.4 本章小结..................................................................................................................................17
第三章 AdaBoost 算法和直方图匹配原理................................................................................18
3.1 特征与特征值计算...................................................................................................................18
3.1.1 矩形特征.............................................................................................................18
3.1.2 积分图.................................................................................................................19
3.2 AdaBoost 分类器......................................................................................................................22
3.2.1 PAC 学习模型.....................................................................................................22
3.2.2 弱学习与强学习.................................................................................................22
3.2.3 AdaBoost 算法....................................................................................................23
3.2.4 弱分类器.............................................................................................................25
3.2.5 弱分类器的训练及选取......................................................................................27
3.2.6 强分类器.............................................................................................................28
3.2.7 级联分类器.........................................................................................................28
3.3 人脸匹配原理(直方图匹配)...............................................................................................31
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毕业设计报告纸
3.3.1 直方图的均衡化..................................................................................................31
3.3.2 灰度变换.............................................................................................................32
3.4 本章小结..................................................................................................................................33
第四章 基于 Andriod 平台的人脸识别系统实现......................................................................35
4.1 Android 系统平台....................................................................................................................35
4.2 开发环境搭建..........................................................................................................................37
4.2.2 OpenCV 介绍......................................................................................................37
4.2.3 OpenCV 编译移植..............................................................................................38
4.3 整体设计..................................................................................................................................38
4.4 应用软件设计..........................................................................................................................39
第五章 软件实现和测试............................................................................................................41
5.1 软件实现..................................................................................................................................41
5.1.1 软件实现过程.....................................................................................................41
5.1.2 建立 UI 界面.......................................................................................................41
5.1.3 JAVA 平台程序开发...........................................................................................42
5.1.4 JNI 层函数接口...................................................................................................43
5.1.5 编写脚本文件.....................................................................................................43
5.2 软件测试..................................................................................................................................44
5.2.1 实验环境.............................................................................................................44
5.2.2 实验结果.............................................................................................................44
5.3 人脸识别..................................................................................................................................45
5.3.1 图片抓取.............................................................................................................45
5.3.2 实验结果.............................................................................................................45
第六章 小结与展望....................................................................................................................46
6.1 总结..........................................................................................................................................46
6.2 展望..........................................................................................................................................46
致 谢............................................................................................................................................48
参 考 文 献..................................................................................................................................49
II
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资源评论
- 上溪月moonlight2014-08-26谢谢分享!学到了好多东西
- nolouch2015-03-17很好的参考价值,正在做这个,谢谢分享
u010211043
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