PCA安检面部识别系统研究+文献综述(2)
This article has taken the eigenvalue extraction methods based on wavelet transform combine with discrete cosine transform and the PCA(Principal Component Analysis) which is simple, rapid and easy wait for a characteristic, can reflect from whole face ,image gray correlation has certain practical value.
The feature extraction based on the combining of wavelet transform and discrete cosine transform is used in the paper．Image is decomposed using wavelet transform and its low frequency part reserves the majority of information and energy．At the same time，the relatively larger feature vector modulus is generated in the sensitive location of the image after the wavelet transform．Discrete cosine transform is an orthogonal transformation．A variety of orthogonal transformation can reduce the relevance of random vector in a certain extent and the energy will be concentrated on a small number of transform coefficients when the signal was transformed by most of the orthogonal transformation．This can be proved in the mathematics．Those are useful for face recognition when these advantages were used in face image
Based on above theory: the face recognition technology could be applied in many fields. For example：the access control system．First we can preprocess the given face image. Second feature value is extracted．At last，comparing with face image and the model in the face database to determine whether it belongs to the face database．If so access control system will be opened and this can realize the purpose of automatic recognition．The results of experiment indicate the new method of the technology of human face recognition is available．
Key words: face recognition；image preprocessing；feature extraction；wavelet transform；PCA
目录
摘要 1
Abstract 2
第一章 绪论 6
1.1 背景和意义 6
1.2 发展现状 7
1.3 研究历史及常见的识别方法 8
1.4 人脸识别技术所面临的问题 10
1.5 论文结构安排 10
第二章 MATLAB简介 11
第三章 人脸识别方法与理论 12
3.1人脸识别概述 12
3.1.1人脸识别的实现过程 12
3.1.2人脸识别技术的优点和难点 13
3.1.3人脸识别技术的分类 14
3.2特征值提取及方法 14
3.2.1人脸图像特征值提取 14
3.2.2特征提取的方法 15
3.3距离度量和分类器 19
3.3.1距离度量 19
3.3.2分类器 19
3.4本章小结 20
第四章 人脸图像的预处理 20
4.1彩色图像处理 20
4.2灰度归一化 21
4.3灰度图像平滑与锐化处理 23
4.4定位人脸 24
4.5几何归一化 26
4.6本章小结 27
第五章 人脸图像的特征值提取 27
5.1小波变换的基本概念 27
5.1.1连续小波变换 28
5.1.2离散小波变换 29
5.1.3多分辨率分析 29
5.1.4小波包和多小波理论 30
5.2离散余弦变换（DCT） 32
5.3基于DWT和DCT的特征值提取 32
5.4 PCA方法 32
5.4.1K-L变换 32
5.4.2特征值提取 33
5.5本章小结 34
第吹冰章 基于MATLAB的人脸识别系统仿真实现 34
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