【毕业论文】基于matlab的人脸识别（含源程序）.pdf
【毕业论文】基于matlab的人脸识别系统设计与仿真（含matlab源程序第一章绪论本章提出了本文的研究背景及应用前景。首先阐述了人脸图像识别意义;然后介绍了人脸图像识别研究中存在的问题;接着介绍了自动人脸识别系统的一般框架构成;最后简要地介绍了本文的主要工作和章节结构。1.1研究背景自70年代以来.随着人工智能技术的兴起.以及人类视觉研究的进展.人们逐淅对人脸图像的机器识别投入很大的热情,并形成了一个人脸图像识别研究领域,这一领域除了它的重大理论价值外,也极具实用价值。在进行人工智能的研究中,人们一直想做的事情就是让机器具有像人类一样的思考能力,以及识别事物、处理事物的能力,因此从解剖学、心理学、行为感知学等各个角度来探求人类的思维机制、以及感知事物、处理事物的机制,并努力将这些机制用于实践,如各种智能机器人的研制。人脸图像的机器识别研究就是在这种背景下兴起的,因为人们发现许多对于人类而言可以轻易做到的事情,而让机器来实现却很难,如人脸图像的识别,语音识别,自然语言理解等。如果能够丿发出具有像人类一样的机器识别机制,就能够逐步地了解人类是如何存储信息,并进行处理的,从而最终了解人类的思维机制。同时,进行人脸图像识别研究也具有很大的使用价依。如同人的指纹一样,人脸也具有唯一性,也可用来鉴别一个人的身份。现在已有实用的计算机自动指纹识别系统面世,并在安检等部门得到应用,但还没有通用成熟的人脸自动识别系统出现。人脸图像的自动识别系统较之指纹识别系统、DNA鉴定等更具方便性,因为它取样方便,可以不接触目标就进行识别,从而开发研究的实际意义更大。并且与指纹图像不同的是,人脸图像受很多因素的干扰:人脸表情的多样性;以及外在的成像过程中的光照,图像尺寸,旋转,姿势变化等。使得同一个人,在不同的坏境下拍摄所得到的人脸图像不同,有时更会有很大的差别,给识别带来很大难度。因此在各种干扰条件下实现人脸图像的识别,也就更具有挑战性。国外对于人脸图像识别的研究较早,现己有实用系统面世,只是对于成像条件要求较苛刻,应用范围也就较窄,国内也有许多科研机构从事这方而的研究,并已取得许多成果。12人脸图像识别的应用前景人脸图像识别除了具有重大的理论价值以及极富挑战性外,还其有许多潜在的应用前景,利用人脸图像来进行身份验证,可以不与目标相接触就取得样本图像,而其它的身份验证手段,如指纹、眼睛虹膜等必须通过与目标接触或相当接近来取得样木,在某些场合,这些识别手段就会有不便之处。就从目前和将米米看,可以预测到人脸图像识别将具有)阔的应用前景,如表1-1中所列举就是其中已经实现或逐步完善的应用。表1-1人脸识别的应用应用优点存在问题信信用卡、汽车图像摄取可控需要建立庞大的驾照、护照以及个人图像分割可控数据库身份验证等图像质量好嫌疑犯照片匹配图像质量不统潜在的巨大图像多幅图像可用库互联网应用视频信息价值高存在虚假多人参与银行/储蓄安全监控效果好图像分割不可控图像质量较差人群监测图像质量高图像分割自由可利用摄像图像图像质量低、实时性13本文研究的问题本文介绍了人脸图像识别中所应用 MATLAB对图像进行预处理,应用该工具箱对图像进行经典图像处理,通过实例来应用 matlab图像处理功能,对某一特定的人脸图像处理,进而应用到人脸识别系统。本文在总结分析人脸识别系统中几种常用的图像预处理方法基础,利用 MATLAB实现了一个集多种预处理方法于一体的通用的人脸图像预处理仿真系统,将该系统作为图像预处理模块可嵌入在人脸识别系统中,并利用灰度图像的直方图比对来实现人脸图像的识别判其中涉及到图像的选取,脸部定位,特征提取,图像处理和识别几个过程。1.4识别系统构成人类似乎具有“与生俱米”的人脸识別能力,赋予计算机同样的能力是人类的梦想之一,这就是所谓的“人脸识别”系统。假设我们把照相机、摄像头、扫描仪等看作计算机的“眼睛”,数字图像可以看作计算机观察到的“影像”,那么AFR赋予计算机根据其所“看到”的人脸图片来判断人物身份的能力。广义的讲,自动人脸识别系统具有如图1.1所示的一般框架并完成相应功能的任务。www.docin.com人脸图像获取人检特征提取人脸识别图1.1人脸识别系统一般框架(1)人脸图像的获取一般来说,图像的获取都是通过摄像头摄取,但摄取的图像可以是真人,也可以是人脸的图片或者为了相对简单,可以不考虑通过摄像头来摄取头像,而是直接给定要识别的图像。(2)人脸的检测人脸检测的任务是判断静态图像中是否存在人脸。若存在人脸,给出其在图像中的坐标位置、人脸区域大小等信息。而人脸跟踪则需要进一步输出所检测到的人脸位置、大小等状态随时间的连续变化情况(3)特征提取通过人脸特征点的检测与标定可以确定人脸图像中显著特征点的位置(如眼睛、眉毛、鼻子、嘴巴等器官),同时还可以得到这些器官及其面部轮廓的形状信息的描述。根据人脸特征点检测与标定的结果,通过某些运算得到人脸特征的描述(这些特征包括:全局特征和局部特征,显式特征和统计特征等)(4)基于人脸图像比对的身份识别In. com即人脸识别( Face Identification)问题。通过将输入人脸图像与人脸数据库中的所有已知原型人脸图像计算相似度并对其排序来给出输入人脸的身份信息。这包括两类识别问题:一类是闭集( Close set)人脸识别问题,即假定输入的人脸一定是人脸库中的某个个体;另类是开集( Open Set识别,即首先要对输入人脸是否在已知人脸库中做出判断,如果是,则给出其身份。(5)基于人脸图像比对的身份验证即人臉确认( Face Verification)问题。系统在输入人臉图像的同时输入一个用户宣称的该人脸的身份信息,系统要对该输入人脸图像的身份与宣称的身份是否相符作出判断。15论文的内容及组织第二章主要介绍人脸识别系统中所用到的仿真软件 Matlab,介绍了在人脸图像识别过程中所需要的图像处理技术,包括:一些基本操作、格式转换、图像增强等。并做了一个 Matlab图像处理功能的实例第三章主要始涉三个方面:首先是对人脸识别系统的构成做详细论述;其次就是对人脸识别过程中的关键环节人脸检测、特征提取和图像预处埋做详细介绍;最后就是 Matlab在人脸识别系统中的具体应用,即人脸图像识别的具体技术,并用 Matlab进行仿真试验并得到结果。第四章是对人脸图像识别体系构架的设计,并给出了人脸识别用到的理论知识即直方图差异对比,并编写 matlab代码实现人脸图像识别。第五章总结了全文的工作并对以后的需要进一步研究的问题进行了展望。第二章图像处理的 Matlab实现21 Matlab简介H Math Work公司开发的 Matlab语言语法限制不严格,程序设计自由度大程序的可移植性好。 Matlab还推出了功能强大的适应于图像分析和处理的工具箱常用的有图像处理工具箱、小波分析工具箱及数字信号处理工具箱。利用这些工具箱我们可以很方便的从各个方面对图像的性质进行深入的研究。 Matlab图像处理工具箱支持索弓图像、RGB图像、灰度图像、二进制图像并能操作.bmp、jg、tif等多种图像格式文件。22数字图像处理及过程n豆丁图像是人类获取信息、表达信息和传递信息的重要手段。利用计算机对图像进行去除噪声、增强、复原、分割、提取特征等的理论方法和技术称为数字图像处理。数字图像处理技术已经成为信息科学、计算机科学、工程科学、地球科学等诸多方面的学者研究图像的有效工具。数字图像处理主要包括图像变换、图像增强、图像编码、图像复原、图像重建、图像识别以及图像理解等内容。221图像处理的基本操作读取和显示图像可以通过 imrad和 ishow来实现;图像的输出用 imwrite0函数就可以很方便的把图像输出到硬盘上;另外还可以用 incrop、 imriEκeO、 imrotateO等来实现图像的裁剪、缩放和旋转等功能。222图像类型的转换Matlab支持多种图像类型,但在某些图像操作中,对图像的类型有要求,所以要涉及到对图像类型进行转换。 Matlab7.0图像处理工具箱为我们提供了不同图像类型相互转换的大量函数,如mat2 gray函数可以将矩阵转换为灰度图像,rgb2 gray转换RGB图像或颜色映像表为灰度图像。在类型转换的时候,我们还经常遇到数据类型不匹配的情况针对这种情况, Matlab7.0工具箱中,也给我们提供了各种数据类型之间的转换函数,如 double(就是把数据转换为双精度类型的函数。223图像增强图像增强的目的是为了改善图像的视觉效果,提高图像的清晰度和工艺的适应性,以及便于人与计算机的分析和处理,以满足图像复制或再现的要求。图像增强的方法分为空域法和频域法两大类,空域法主要是对图像中的各个像紊点进行操作;而频域法是在图像的某个变换域内对整个图像进行操作,并修改变换后的系数,如傅立叶变换、DCT变换等的系数然后再进行反变换,便可得到处理后的图像。下面以空域增强法的几种方法加以说明。(1).灰度变换增强有多种方法可以实现图像的灰度变换,其中最常用的就是直方图
- 2020-12-05下载
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Lectures on Stochastic Programming-Model
这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way
- 2020-12-09下载
- 积分：1
