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人脸识别原理及算法：动态人脸识别系统研究

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人脸识别原理及算法：动态人脸识别系统研究

Name: 人脸识别原理及算法：动态人脸识别系统研究
Brand: Jingdong book
SKU: 3118085891
Price: 53.97 USD
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本书系统地总结了人脸识别研究领域，填补国内有关该领域图书的空白，很好地总结了近年人脸识别算法研究成果，并提供了具体算法实现和研究结果，为该领域研究人员提供很好的借鉴。从事人脸识别研究的读者结合本书及最新的相关科技期刊进行人脸识别研究，可以大大地减少研究前期为进入本领域而进行论文阅读和相关论文总结所需要的时间。目前各地对人脸识别研究更加关注和投入，出版这本图书可为相关领域研究人员提供便捷工具，并提高研究机构在该领域研究的普及化。

Content Description

《人脸识别原理及算法：动态人脸识别系统研究》系统介绍了人脸识别研究领域的研究状况以及作者在人脸识别领域的研究工作和研究成果，全书共分为3个部分。
第1部分首先介绍了人脸识别的基础：计算机视觉和模式识别的原理，并介绍了20世纪70年代以来国内外人脸识别研究的研究动态和主要方法，以及国内外人脸识别研究的主要成果和用途。
第2部分介绍了基于双属性图的人脸识别算法，该算法采用人脸特征检测、主成分分析方法、Gabor函数等建立了一个人脸特征识别和属性特征匹配的人脸识别方法，并结合人脸图像的局部特征和全局特征，能够有效地利用从三维到二维投影的人脸图像信息之间的关联性。
第3部分介绍了动态场景下的人脸识别方法，该方法综合应用了人脸定位、人脸识别、视频处理等算法。
《人脸识别原理及算法：动态人脸识别系统研究》的读者对象主要为研究模式识别的科技人员以及高等院校高年级的学生和研究生。读者通过阅读《人脸识别原理及算法：动态人脸识别系统研究》可以系统地学习人脸识别研究的方法，并掌握国内外相关技术的最新进展。

Author Description

沈理，1959年毕业于浙江大学机电工程系，当前是中科院计算技术研究所研究员、博士生导师，从事计算机科学领域的研究工作。

刘翼光，2000年毕业于中科院计算技术研究所，曾主持或参加多项国家自然科学基金、中科院“九五”重点项目、国家863项目等。

熊志勇，于1999年在中科院研究所获得博士学位，研究方向为人脸图像识别。母亲啊从事线通信基站的设计研发工作。

Catalogue

第1部分人脸识别介绍
第1章人脸识别概论
1.1 历史背景
1.2 人脸识别相关学科的进展
1.2.1 神经生理学方面的进展
1.2.2 脑神经学方面的进展
1.2.3 计算机视觉方面的进展
1.3 模式识别理论
1.3.1 预处理
1.3.2 特征提取
1.3.3 分类
1.4 人脸图像识别主要研究的问题
1.4.1 数据采样
1.4.2 干扰因素
1.5 人脸图像识别系统的构成
1.5.1 人脸图像预处理
1.5.2 图像表示与特征提取
1.5.3 图像识别
第2章人脸识别研究综述
2.1 研究现状
2.2 常用的静态人脸图像识别方法
2.2.1 几何特征方法
2.2.2 特征脸方法
2.2.3 局部特征分析技术
2.2.4 模板匹配
2.2.5 图匹配
2.2.6 人工神经网络方法
2.2.7 柔性形状模型技术
2.2.8 综合的方法
2.3 人脸检测以及人脸跟踪
2.4 人脸图像识别的应用前景
2.5 一些商用人脸识别软件
2.5.1 TrueFace
2.5.2 Face-It
2.5.3 Technology Recognition Systems

第2部分静态人脸图像识别
第3章人脸特征探测
3.1 简介
3.2 参数化模型法
3.2.1 图像的各种表示
3.2.2 眼睛模型表示
3.2.3 嘴巴模型和鼻子模型
3.2.4 方法的优缺点
3.3 基于模板的探测方法
3.3.1 模板表示
3.3.2 图像标准化
3.3.3 方法的优缺点
3.4 利用数学算子进行探测
3.4.1 特征探测模型
3.4.2 方法的优缺点
3.5 小结
第4章基于通用形变模型的人脸轮廓特征提取
4.1 引言
4.2 通用形变模型的形成
4.2.1 轮廓特征点的选取
4.2.2 形变模型的形成
4.2.3 形变模型的能量
4.3 模型匹配
4.3.1 全局匹配
4.3.2 局部匹配
4.4 实验
4.5 小结
第5章基于主成分分析方法的人脸图像识别及人脸局部特征探测
5.1 引言
5.2 主成分分析方法在人脸图像识别中的应用
5.2.1 特征向量的表示能力
5.2.2 特征向量的选择
5.2.3 光照的影响
5.2.4 尺度的影响
5.2.5 旋转因素的影响
5.2.6 小结
5.3 人脸局部特征探测
5.3.1 逐步求精定位法
5.3.2 实验
5.4 利用局部特征识别人脸图像
5.5 小结
第6章人脸图像的双属性图表示
6.1 引言
6.2 Gabor变换
6.2.1 傅里叶变换
6.2.2 Gabor变换
6.3 基于数学变换的特征提取
6.3.1 特征的生成
6.3.2 实验
6.3.3 实验结果
6.3.4 小结
6.4 人脸图像的双属性图表示
6.4.1 关系图
6.4.2 属性图
6.4.3 双属性图
6.5 小结
第7章人脸图像识别
7.1 引言
7.2 待识人脸图像的表示
7.2.1 局部特征点的确定
7.2.2 局部主成分特征的确定
7.2.3 Gabor系数特征的确定
7.3 双属性图匹配
7.3.1 匹配函数
7.3.2 lt的确定
7.3.3 光照的处理
7.3.4 尺度的处理
7.3.5 平面旋转人脸图像的处理
7.3.6 深度旋转人脸图像的处理
7.3.7 图像识别
7.3.8 方法的有效性
7.4 实验
7.4.1 图像尺度及旋转情况的确定
7.4.2 人脸图像识别
7.5 小结
第8章人脸图像识别程序实现
8.1 主成分分析算法的MATLAB实现
8.2 Gabor变换
8.3 使用动态模板方法进行识别
8.4 Gauss变换用于提取图像的特征

第3部分动态人脸识别研究
第9章动态人脸识别系统
9.1 研究背景及条件
9.1.1 研究背景
9.1.2 研究对象和限定条件
9.2 样本获取
9.2.1 视频格式
9.3 隐马尔科夫模型
9.4 动态人脸识别系统框架
9.4.1 预处理
9.4.2 相关性处理
9.4.3 隐马尔科夫模型的状态及属性确定
9.4.4 观察序列概率分布计算
9.4.5 隐马尔科夫模型的训练
9.4.6 隐马尔科夫模型的识别
第10章动态人脸图像定位算法
10.1 动态人脸图像定位问题
10.2 Hausdorff距离
10.3 人脸图像定位算法
10.4 实验结果
第11章动态人脸识别的相关性
11.1 人脸图像的相关性表示
11.2 动态人脸的相关系数
11.2.1 特征子空间相关系数
11.2.2 插值相关系数
11.2.3 图像内部信息相关系数
11.2.4 3种相关系数的实验比较
11.3 相关性处理与动态图像分割
第12章动态人脸图像识别
12.1 隐马尔科夫模型的基本问题
12.1.1 估价问题和前向算法
12.1.2 解码问题和Viterbi算法
12.1.3 训练问题
12.1.4 识别问题
12.2 基于隐马尔科夫模型的动态人脸识别系统
12.2.1 隐马尔科夫模型的状态及属性图像
12.2.2 观察序列数目
12.2.3 观察序列初始概率分布
12.2.4 相关系数与初始概率调整
12.2.5 隐马尔科夫模型训练和动态人脸图像识别
12.3 实验结果与分析
12.3.1 实验结果
12.3.2 与相关实验结果的对比
12.4 隐马尔科夫模型与动态图像分割
12.5 动态人脸图像识别中外界影响的处理
12.5.1 有外界影响的动态人脸识别
12.5.2 动态人脸识别算法在外界影响下的调整
第13章结束语
参考文献
术语

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