人脸图像的二维线性判别分析研究.doc
约38页DOC格式手机打开展开
人脸图像的二维线性判别分析研究,2.2万字本人今年最新原创的毕业设计，仅在本站独家提交，大家放心使用摘要 随着图像处理技术在实际工程中的不断应用，图像处理分析日渐成为一门引人注目、前景远大的技术。人工智能的提出，使得人们对于计算机能够像自己一样具有识别分析能力有了很大的期待。而如何使得计算机具有识别理解图像能力成为近年来...
内容介绍
此文档由会员 jiji888 发布
人脸图像的二维线性判别分析研究
2.2万字
本人今年最新原创的毕业设计，仅在本站独家提交，大家放心使用
摘要 随着图像处理技术在实际工程中的不断应用，图像处理分析日渐成为一门引人注目、前景远大的技术。人工智能的提出，使得人们对于计算机能够像自己一样具有识别分析能力有了很大的期待。而如何使得计算机具有识别理解图像能力成为近年来模式识别以及图像处理领域的研究热点。
人脸图像的二维线性分析是运用基于二维线性方法分析归类人脸图像的研究，其中对于人脸图像的分析具有重要意义，其丰富的信息量为区别个体信息边界，目标识别创造了优越的条件，与其他方法相比，人脸图像识别具有极大的优越性。随着人脸图像的分辨率大幅度的提高以及信息集成化的迅速发展，使得对人脸图像处理的要求越来越高。
本文采用了二维线性分析方法，其中运用比较了多种二维图像处理方法，例如：二维线性判别分析(2DLDA)、二维主成分分析(2DPCA)、二维非相关判别分析(2DUDA)、双向二维线性判别分析(2D2LDA)等方法，通过比较采用了双向二维线性判别分析法对人脸图像进行特征提取，其任务是从大量的原始数据中找到最能代表该图像的少量特征。其次是运用最近邻方法将人脸图像分类，其工作原理是寻找到被分类对象训练、测试数据集中的k个最近邻域数据，然后根据这些最近邻域数据分类属性对目标进行预测，将得到的预测值赋给被分类对象的分类属性。从而在类别决策时，只有极少量的相邻样本相关。其具体的操作是通过MATLAB的编程、调试、运行来实现的，得到二维线性压缩的图像，与原有的图像比较，并输出最终的识别率。
关键词： 二维线性分析 最近邻法 图像识别 MATBLE
Abstract Along with image processing technology in the practical engineering applications, image processing analysis has become a compelling, prospects great technology.Artificial intelligence is put forward that enables the people for the computer to like themselves with identification analysis ability have great expectations.And how to make computer has the identification ability to read images become in recent years the research focus in the field of pattern recognition and image processing.
Face image of two-dimensional linear analysis is using the method based on two-dimensional linear classification research of face image, which is of great significance for face image analysis, its abundant information for the difference between individual information boundaries, target recognition has created favorable conditions, compared with other methods, human face image recognition has a great advantage.With the improvement of the resolution of the face image greatly, and the rapid development of information integration, making more and more high to the requirement of human face image processing.
This paper adopts the two-dimensional linear analysis method, which using the comparison of the variety of two-dimensional image processing methods, such as: 2DLDA, 2DPCA, 2DUDA, 2D2LDA method, through comparing the 2D2LDA method of face images for feature extraction, the mission from lots of original data to find the most can represent the image of a small amount of features.Using the nearest neighbor method followed by face image classification, its working principle is to look for to be classified object of training and testing data set k nearest neighbor domain data, and then based on the nearest neighbor domain data classification properties forecast target, will get the predicted values assigned to the category attributes of the object being classified.To the decision, the category only very small amounts of the adjacent sample correlation.Its concrete operation is accomplished by MATLAB programming, debugging and running, two-dimensional linear compressed image, compared with the original image, and the output of the final recognition rate.
Key words: Two dimensional linear analysis Nearest neighbor method Image recognition MATLAB
目 录
摘要…………………………………………………………………………………………I
目录…………………………………………………………………………………………III
第一章 绪论………………………………………………………………………………1
1.1 生物特征识别技术概述……………………………………………………………1
1.2 人脸识别技术优缺点…………………………………………………………………2
1.2.1 人脸识别技术优势………………………………………………………………2
1.2.2 人脸识别技术劣势………………………………………………………………3
1.3人脸识别研究背景及研究意义……………………………………………………3
1.4 国内外研究概述………………………………………………………………………4
1.5 本论文研究简介及组织结构…………………………………………………………5
第二章 二维人脸图像的分析……………………………………………………………6
2.1 二维人脸图像处理系统组成………………………………………………………6
2.2 二维人脸图像的预处理……………………………………………………………7
2.3 二维人脸图像检测…………………………………………………………………8
2.4 二维人脸图像的特征提取…………………………………………………………8
2.5 二维人脸识别分类…………………………………………………………………9
第三章 二维线性分析方法以及最近邻分类法……………………………10
3.1..
2.2万字
本人今年最新原创的毕业设计，仅在本站独家提交，大家放心使用
摘要 随着图像处理技术在实际工程中的不断应用，图像处理分析日渐成为一门引人注目、前景远大的技术。人工智能的提出，使得人们对于计算机能够像自己一样具有识别分析能力有了很大的期待。而如何使得计算机具有识别理解图像能力成为近年来模式识别以及图像处理领域的研究热点。
人脸图像的二维线性分析是运用基于二维线性方法分析归类人脸图像的研究，其中对于人脸图像的分析具有重要意义，其丰富的信息量为区别个体信息边界，目标识别创造了优越的条件，与其他方法相比，人脸图像识别具有极大的优越性。随着人脸图像的分辨率大幅度的提高以及信息集成化的迅速发展，使得对人脸图像处理的要求越来越高。
本文采用了二维线性分析方法，其中运用比较了多种二维图像处理方法，例如：二维线性判别分析(2DLDA)、二维主成分分析(2DPCA)、二维非相关判别分析(2DUDA)、双向二维线性判别分析(2D2LDA)等方法，通过比较采用了双向二维线性判别分析法对人脸图像进行特征提取，其任务是从大量的原始数据中找到最能代表该图像的少量特征。其次是运用最近邻方法将人脸图像分类，其工作原理是寻找到被分类对象训练、测试数据集中的k个最近邻域数据，然后根据这些最近邻域数据分类属性对目标进行预测，将得到的预测值赋给被分类对象的分类属性。从而在类别决策时，只有极少量的相邻样本相关。其具体的操作是通过MATLAB的编程、调试、运行来实现的，得到二维线性压缩的图像，与原有的图像比较，并输出最终的识别率。
关键词： 二维线性分析 最近邻法 图像识别 MATBLE
Abstract Along with image processing technology in the practical engineering applications, image processing analysis has become a compelling, prospects great technology.Artificial intelligence is put forward that enables the people for the computer to like themselves with identification analysis ability have great expectations.And how to make computer has the identification ability to read images become in recent years the research focus in the field of pattern recognition and image processing.
Face image of two-dimensional linear analysis is using the method based on two-dimensional linear classification research of face image, which is of great significance for face image analysis, its abundant information for the difference between individual information boundaries, target recognition has created favorable conditions, compared with other methods, human face image recognition has a great advantage.With the improvement of the resolution of the face image greatly, and the rapid development of information integration, making more and more high to the requirement of human face image processing.
This paper adopts the two-dimensional linear analysis method, which using the comparison of the variety of two-dimensional image processing methods, such as: 2DLDA, 2DPCA, 2DUDA, 2D2LDA method, through comparing the 2D2LDA method of face images for feature extraction, the mission from lots of original data to find the most can represent the image of a small amount of features.Using the nearest neighbor method followed by face image classification, its working principle is to look for to be classified object of training and testing data set k nearest neighbor domain data, and then based on the nearest neighbor domain data classification properties forecast target, will get the predicted values assigned to the category attributes of the object being classified.To the decision, the category only very small amounts of the adjacent sample correlation.Its concrete operation is accomplished by MATLAB programming, debugging and running, two-dimensional linear compressed image, compared with the original image, and the output of the final recognition rate.
Key words: Two dimensional linear analysis Nearest neighbor method Image recognition MATLAB
目 录
摘要…………………………………………………………………………………………I
目录…………………………………………………………………………………………III
第一章 绪论………………………………………………………………………………1
1.1 生物特征识别技术概述……………………………………………………………1
1.2 人脸识别技术优缺点…………………………………………………………………2
1.2.1 人脸识别技术优势………………………………………………………………2
1.2.2 人脸识别技术劣势………………………………………………………………3
1.3人脸识别研究背景及研究意义……………………………………………………3
1.4 国内外研究概述………………………………………………………………………4
1.5 本论文研究简介及组织结构…………………………………………………………5
第二章 二维人脸图像的分析……………………………………………………………6
2.1 二维人脸图像处理系统组成………………………………………………………6
2.2 二维人脸图像的预处理……………………………………………………………7
2.3 二维人脸图像检测…………………………………………………………………8
2.4 二维人脸图像的特征提取…………………………………………………………8
2.5 二维人脸识别分类…………………………………………………………………9
第三章 二维线性分析方法以及最近邻分类法……………………………10
3.1..
