【作者】 赵晶；

【导师】 魏小鹏； 张强；

【作者基本信息】 大连大学 ， 计算机应用技术， 2008， 硕士

【摘要】 运用人体本身所固有的生物特征进行个人身份鉴别的技术是与传统方法完全不同的崭新技术,具有更好的安全性、可靠性和有效性,越来越受到人们的重视。人脸作为一种独特的生物特征,以其特有的直接性、唯一性和方便性等特点被愈来愈广泛的应用在身份识别的领域。人脸识别是指基于已知的人脸样本集,利用计算机分析比较人脸图像中的人脸视觉特征信息,运用图像处理和模式识别等技术从静态或动态场景中获得并分析一个或多个人脸,然后从中提取出有效的识别信息,来自动鉴别图像中待识别人的身份的一门技术。它涉及了信号处理、智能控制、模式识别和机器视觉等多个学科的知识,具有很高的理论价值,逐渐成为了计算机视觉与模式识别领域研究的热点问题之一。而随着智能化信息处理技术的发展,人脸识别在法律、商业、安全系统等方面也有着日益广泛的应用。本文的工作如下:1.在对各种人脸识别方法进行分析的基础上,本文重点研究了基于隐马尔可夫模型(Hidden Markov Models,简称为HMM)的人脸识别方法。在已有的HMM方法的基础上,提出了一种基于水平积分投影函数(Integral Projection Functions,简称为IPF)和HMM的人脸识别方法。该方法首先将人脸图像进行积分投影,然后利用投影后的代表原始图像不同细节特征的投影向量建立HMMs,最后进行训练和识别,在ORL人脸数据库中的试验结果表明该方法提高了识别率。2.由于基于HMM的人脸识别方法建立在统计模型的基础上,其识别结果由各个模型的输出概率来确定,因此本文提出了一套将Fisher线性鉴别分析(Fisher Linear Discriminate Analysis,简称为FLDA),复主成分分析(Principal Analysis in the Complex Space,简称为CPCA)和HMM相结合的用于正面人脸识别的方法。该方法首先对输入的不同光照、人脸表情和姿势的图像进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间,通过运用CPCA来提取人脸图像的有效鉴别特征,最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行试验,试验结果表明该方法有较好的识别率。更多还原

【Abstract】 Personal identification system based on using the proper living creature characteristic of human body is the totally brand-new technique, is different from traditional methods and has the better safety, dependability and usefulness, so more and more people begin to think much of it. Person’s face which has the directness, uniqueness and convenience et al is a unique living characteristic. Face recognition can be simply defined as follows: given a certain static image or dynamic video of scene, to detect and recognize one or more persons on the basis of prearranged face database, then to recognize who is(are) the person(s). It covers knowledge of many subjects, such as signal processing, intelligence control, pattern recognition, machine vision et al, has very high theoretic values, and becomes a very active research topic in computer vision and computer pattern recognition. With the development of intelligent information and processing, face recognition will be broadly applied in law, business, security system, and so on.The main work primarily includes:1. On the basis of several kinds of face recognition methods, HMM is mainly studied. A new algorithm which focuses on the use of Integral Projection Functions and Hidden Markov Models for face recognition is presented. First, the Integral Projection values of face images are computed. Second, these Integral Projection values which are translated into one- dimension vector sequences are trained and recognized by HMM. The result of experiment on ORL database shows the improved recognition rate.2. A new algorithm which focuses on the use of Fisher Linear Discriminate Analysis, Principal Analysis in the Complex Space and Hidden Markov Models for face recognition is presented. First, the different images are translated into one-dimension vector sequences with the same mean and variance. Second, FLDA is used to get the features of the images and complex vector space, CPCA is applied to get the new features, then these new features are trained by HMMs. Finally, an optimized HMM is obtained. Compared with other face recognition algorithms on the ORL face database, this method can get better recognition rate.更多还原