Precise face model adaptation for semantic coding of videophone sequences

图像检测外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)译文基于半边脸的人脸检测概要：图像中的人脸检测是人脸识别研究中一项非常重要的研究分支。

为了更有效地检测图像中的人脸，此次研究设计提出了基于半边脸的人脸检测方法。

根据图像中人半边脸的容貌或者器官的密度特征，比如眼睛，耳朵，嘴巴，部分脸颊，正面的平均全脸模板就可以被构建出来。

被模拟出来的半张脸是基于人脸的对称性的特点而构建的。

图像中人脸检测的实验运用了模板匹配法和相似性从而确定人脸在图像中的位置。

此原理分析显示了平均全脸模型法能够有效地减少模板的局部密度的不确定性。

基于半边脸的人脸检测能降低人脸模型密度的过度对称性，从而提高人脸检测的速度。

实验结果表明此方法还适用于在大角度拍下的侧脸图像，这大大增加了侧脸检测的准确性。

关键词：人脸模板，半边人脸模板，模板匹配法，相似性，侧脸。

I.介绍近几年，在图像处理和识别以及计算机视觉的研究领域中，人脸识别是一个很热门的话题。

作为人脸识别中一个重要的环节，人脸检测也拥有一个延伸的研究领域。

人脸检测的主要目的是为了确定图像中的信息，比如，图像总是否存在人脸，它的位置，旋转角度以及人脸的姿势。

根据人脸的不同特征，人脸检测的方法也有所变化[1-4]。

而且，根据人脸器官的密度或颜色的固定布局，我们可以判定是否存在人脸。

因此，这种基于肤色模型和模板匹配的方法对于人脸检测具有重要的研究意义[5-7]。

这种基于模板匹配的人脸检测法是选择正面脸部的特征作为匹配的模板，导致人脸搜索的计算量相对较大。

然而，绝大多数的人脸都是对称的。

所以我们可以选择半边正面人脸模板，也就是说，选择左半边脸或者有半边脸作为人脸匹配的模板，这样，大大减少了人脸搜索的计算。

II.人脸模板构建的方法人脸模板的质量直接影响匹配识别的效果。

为了减少模板局部密度的不确定性，构建人脸模板是基于大众脸的信息，例如，平均的眼睛模板，平均的脸型模板。

这种方法很简单。

面部特征交换实验方法引言面部特征交换实验是一种通过计算机技术实现人脸图像间特征互换的研究领域。

该方法可以在不改变人物身份和外貌特征的基础上，将一个人的面部特征转移到另一个人的面部图像上，从而实现面部特征的交换，具有重要的应用价值。

本文将介绍面部特征交换实验的方法及其应用。

人脸特征提取与标定在进行面部特征交换实验前，首先需要对人脸图像进行特征提取与标定。

特征提取是指从人脸图像中提取出与人脸相关的特征信息，如面部轮廓、眼睛位置、嘴巴位置等。

常用的特征提取方法包括基于深度学习的方法和传统的计算机视觉方法。

对于基于深度学习的方法，通常使用卷积神经网络（CNN）进行特征提取。

通过训练CNN模型，可以从人脸图像中学习到高层次的特征表示。

常用的CNN模型有VGG、ResNet等。

在进行面部特征交换实验时，可以使用预训练好的CNN模型进行特征提取。

传统的计算机视觉方法主要利用人脸识别算法进行特征提取。

常用的人脸识别算法包括特征点标定、轮廓提取、纹理提取等。

这些算法可以通过检测人脸的关键点、外观、形状等特征信息进行面部特征提取。

面部特征对齐与变形在进行面部特征交换实验时，需要对两个人脸图像进行特征对齐和变形。

特征对齐是指将两个人脸图像中的面部特征对应到同一位置，使得它们之间的对应关系是准确的。

特征对齐常用的方法有：1.利用人脸关键点进行对齐：提取人脸图像中的关键点（例如眼睛、鼻子、嘴巴等），通过将两张图像中的关键点进行对应，计算得到他们之间的变换关系（如旋转、平移、缩放等），从而实现面部特征对齐。

2.利用人脸纹理进行对齐：提取人脸图像中的纹理特征，通过计算纹理之间的相似度，找到两张图像中纹理最相似的部分，并将其对齐。

面部特征对齐完成后，还需要进行面部特征的变形。

变形主要包括形状变形和纹理变形。

形状变形是指将一个人的面部特征变形成另一个人的特征，使得两个人的面部形状尽可能相似。

纹理变形是指将一个人的面部纹理变形成另一个人的纹理，使得两个人的面部纹理尽可能相似。

基于多尺度和多方向特征的人脸超分辨率算法黄丽;庄越挺;苏从勇;吴飞【期刊名称】《计算机辅助设计与图形学学报》【年(卷),期】2004(016)007【摘要】提出一个基于学习的人脸图像超分辨率算法.该算法采用可操纵金字塔学习人脸图像中的低层次局部特征的空间分布,并结合塔状的父结构和局部最优匹配算法来预测最佳先验模型;然后将先验模型结合到贝叶斯最大后验概率框架中;最后使用最速下降法求出最优的高分辨率人脸图像.实验结果表明,该算法生成的高分辨率人脸图像具有较好的视觉效果.【总页数】9页(P953-961)【作者】黄丽;庄越挺;苏从勇;吴飞【作者单位】浙江大学计算机科学与技术学院,杭州,310027;浙江大学-微软视觉感知联合实验室,杭州,310027;浙江大学计算机科学与技术学院,杭州,310027;浙江大学-微软视觉感知联合实验室,杭州,310027;浙江大学计算机科学与技术学院,杭州,310027;浙江大学-微软视觉感知联合实验室,杭州,310027;浙江大学计算机科学与技术学院,杭州,310027;浙江大学-微软视觉感知联合实验室,杭州,310027【正文语种】中文【中图分类】TP391【相关文献】1.基于2D-PCA特征描述的非负权重邻域嵌入人脸超分辨率重建算法 [J], 曹明明;干宗良;崔子冠;李然;朱秀昌2.Shearlet多方向特征融合与加权直方图的人脸识别算法 [J], 周霞;张鸿杰;王宪3.一种基于多尺度 LBPH 特征的快速人脸识别算法 [J], 朱峰4.基于多尺度Retinex算法结合PCA特征加权的人脸识别方法 [J], 于梦;云利军;李艾瞳5.多方向多尺度Gabor特征表示及其匹配算法 [J], 周德龙; 张捷; 朱思聪因版权原因，仅展示原文概要，查看原文内容请购买。

①现代的人脸识别，特指通过分析、比较人脸视觉特征信息进行身份鉴别的计算机技术。

具体而言，就是通过视频采集设备获取识别对象的面部图像，再利用核心的算法对其脸部的五官位置、脸型和角度进行计算分析，进而和自身数据库里已有的范本进行比对，最后判断出用户的真实身份。

这是一项高端的计算机图像处理技术。

②在全球范围内，人脸识别系统的研究始于20 世纪60 年代。

人脸识别的优势在于其自然性和不被被测个体察觉的特点。

所谓自然性，是指该识别方式同人类（甚至其他生物）进行个体识别时所利用的生物特征相同。

人脸识别就是通过观察比较人脸来区分和确认身份的。

不被察觉的特点会使该识别方法不令人反感，并且因为不容易引起人的注意而不易被欺骗。

相对于指纹识别而言，人脸识别还具有非接触式（非侵犯式）的特点，因此更加友好、自然，更易被人们接受。

③随着科技的发展，人脸识别技术的应用已经不是什么新鲜事了。

关于面孔知觉的英文文献Face Perception: A Cognitive and Computational Perspective.Introduction.Face perception is a fundamental aspect of human cognition, enabling us to recognize and interact with others, navigate social environments, and express emotions. It is a complex process that involves multiple cognitive and computational mechanisms, including visual processing, attention, memory, and decision-making.Visual Processing.The visual system plays a crucial role in face perception. When we encounter a face, the eyes first scan the central features, such as the eyes, nose, and mouth. This information is then rapidly processed by the visual cortex, which extracts key features and attributes, such asfacial shape, size, and texture.Attention.Attention is essential for focusing on specific aspects of a face, such as the eyes or mouth, which conveyimportant social and emotional information. Attention canbe influenced by a range of factors, including task demands, social cues, and personal preferences.Memory.Memory plays a vital role in face recognition. We store representations of familiar faces in our memory, which allows us to recognize them even after long periods of time. These representations include both structural information about facial features and associated semantic information, such as names and relationships.Decision-Making.Face perception ultimately involves making decisionsabout identity, emotion, and social intentions. These decisions are based on the integration of visual, attentional, and memory processes. For example, when we see a friend's face, we may recognize them based on their unique facial features and associate them with a name and personality.Computational Models.Computational models have been developed to simulate the processes involved in face perception. These models aim to explain how different cognitive and computational mechanisms interact to produce accurate and meaningful representations of faces. Some of the most widely used models include:Face Recognition Networks: These models use machine learning algorithms to learn the features that distinguish different faces. They can be trained on large datasets of images and achieve impressive performance on face recognition tasks.Geometric Face Models: These models represent faces as 3D objects, allowing for the extraction of detailed structural information. They can be used for facial animation, facial reconstruction, and other applications.Statistical Face Models: These models capture the statistical regularities of faces, such as the typical shape and distribution of facial features. They can be used to generate realistic face images and for facial recognition tasks.Applications.Face perception has numerous applications in various fields, including:Security and Surveillance: Face recognition systems can be used for access control, surveillance, and criminal identification.Medical Diagnosis: Facial features can provide clues about certain medical conditions, such as genetic disordersand neurological damage.Human-Computer Interaction: Face-tracking technology allows computers to interact with users in a more natural and intuitive way.Social Psychology: Face perception plays a critical role in social interactions, such as forming impressions, understanding emotions, and establishing relationships.Conclusion.Face perception is a complex and multifaceted cognitive process that involves visual processing, attention, memory, and decision-making. Computational models have provided valuable insights into the mechanisms underlying face perception, leading to numerous applications in various fields. As research continues, our understanding of this fundamental aspect of human cognition will continue to deepen.。

写一篇对于面部识别技术的认识英语作文Facial recognition technology, also known as face recognition technology, is a biometric technology that analyzes and identifies individuals based on their facial features. It has gained significant attention and widespread use in recent years due to its potential applications in various fields. In this article, we will explore the concept of facial recognition technology, its benefits, and potential concerns.Firstly, let's delve into how facial recognition technology works. It involves capturing an individual's facial image or video and analyzing it using various algorithms. These algorithms extract unique facial features such as the distance between the eyes, the shape of the nose, and the contour of the face. These features are then converted into a numerical code, commonly referred to as a faceprint or facial template. This code is compared against a database of known faces to identify the individual.One of the key benefits of facial recognition technology is its potential to enhance security and safety. It can be used in surveillance systems to identify and track individuals in real-time, aiding in the prevention and investigation of criminal activities. For example, it can help law enforcement agencies in identifying suspects or locating missing persons. Additionally, facial recognition technology can be integrated into access control systems, replacing traditional methods such as ID cards or passwords, providing a more secure and convenient way of authentication.Moreover, facial recognition technology has found applications in various industries. In the retail sector, it can be used to analyze customer demographics and behavior, helping businesses tailor their marketing strategies and improve customer experiences. In the healthcare industry, it can assist in patient identification, reducing the risk of medical errors and improving the efficiency of healthcare delivery. Furthermore, facial recognition technology has been utilized in the entertainment industry for personalized experiences, such as unlocking exclusive content or customizing avatars in video games.However, despite its potential benefits, facial recognition technology also raises concerns regarding privacy and ethical implications. The collection and storage of facialdata raise questions about data protection and potential misuse. There are concerns that the technology may be used for mass surveillance, infringing on individuals' right to privacy. Additionally, there have been cases of misidentification or bias in facial recognition systems, particularly when it comes to individuals from diverse racial or ethnic backgrounds. This highlights the importance of ensuring the accuracy and fairness of these systems through rigorous testing and regulation.In conclusion, facial recognition technology holds great promise in various domains, ranging from security to personalized experiences. Its ability to analyze and identify individuals based on their facial features has revolutionized many industries. However, it is crucial to address the concerns surrounding privacy and ethical considerations to ensure its responsible and beneficial use. Striking a balance between innovation and safeguarding individual rights will be key in harnessing the full potential of facial recognition technology.。

新的面部结构英语作文Title: Exploring New Facial Structures。

In recent years, advancements in medical technologyhave brought about revolutionary changes in facial reconstruction procedures. This essay delves into thelatest developments in facial structure reconstruction, highlighting the innovative techniques and their potential implications.One of the most groundbreaking advancements in facial reconstruction is the integration of 3D printing technology. Traditional methods often relied on manual sculpting techniques, which were time-consuming and prone to inaccuracies. However, with the advent of 3D printing, surgeons can now create precise facial implants tailored to each patient's unique anatomy. These implants not only restore aesthetic appearance but also promote functional outcomes, such as improved speech and mastication.Furthermore, computer-aided design (CAD) software plays a pivotal role in customizing facial implants. By precisely mapping the patient's facial contours, surgeons can design implants that seamlessly integrate with existing bone structures. This level of customization enhances both the aesthetic and functional aspects of facial reconstruction, leading to more natural-looking outcomes.In addition to 3D printing, tissue engineering holds promise for revolutionizing facial reconstruction procedures. Scientists are exploring the use of biocompatible scaffolds seeded with patient-derived cells to regenerate facial tissues. This approach offers the potential for tissue regeneration that closely mimics the patient's original facial structure, minimizing the risk of rejection and enhancing long-term outcomes.Moreover, the integration of artificial intelligence (AI) algorithms has streamlined the facial reconstruction process. AI-powered software can analyze medical imaging data to assist surgeons in preoperative planning, precisely predicting the optimal placement of facial implants. Thisnot only reduces surgical time but also enhances surgical precision, ultimately improving patient outcomes.Another emerging trend in facial reconstruction is the use of minimally invasive techniques. Traditional approaches often involved extensive incisions and tissue manipulation, leading to prolonged recovery times and increased risk of complications. However, minimally invasive techniques, such as endoscopic surgery, offer several advantages, including smaller incisions, reduced tissue trauma, and faster recovery times. These techniques are particularly beneficial for patients undergoing facial reconstruction, as they minimize scarring and preserve facial aesthetics.Furthermore, the integration of virtual reality (VR) technology has transformed the patient experience in facial reconstruction. VR simulations allow patients to visualize potential outcomes and actively participate in thedecision-making process. By immersing patients in a virtual environment, surgeons can address any concerns and ensure that the final result aligns with the patient'sexpectations.Despite these advancements, challenges remain in the field of facial reconstruction. One such challenge is achieving optimal tissue integration and vascularization with 3D-printed implants. While biocompatible materials have significantly improved implant success rates, further research is needed to enhance tissue regeneration and long-term implant stability.In conclusion, the field of facial reconstruction is experiencing a paradigm shift driven by technological innovation. From 3D printing and tissue engineering to AI and VR, these advancements are revolutionizing the way surgeons approach facial reconstruction procedures. By leveraging these cutting-edge techniques, surgeons can achieve more natural-looking outcomes while improving patient satisfaction and quality of life.。

Face Model Adaptation using Robust Matching and Active Appearance ModelsF.DornaikaLink¨o ping UniversitySE-58183Link¨o ping,Sweden dornaika@isy.liu.seJ.AhlbergSwedish Defense Research Agency(FOI) SE-58111Link¨o ping,Swedenjorahl@foi.seAbstractThis paper addresses the3D tracking of pose and ani-mation of the human face in monocular image sequences using deformable3D models.For each frame,the proposed adaptation is split into two consecutive stages:global and local.In thefirst stage,the3D pose of the face is recovered using a RANSAC-based technique involving both the con-sensus measure and the consistency with a statistical model of a face texture.In the second stage,the local motion as-sociated with some facial features is recovered using the concept of the active appearance model search.Adaptation examples demonstrate the feasibility and robustness of the developed framework.Keywords:Face model adaptation,active appearance models,real-time tracking,3D pose,animation parameters, matching,robust statistics(RANSAC),learning1.IntroductionImages containing faces are essential to intelligent vision-based human computer interaction.The ability to track facial motion is useful in applications such as face-based biometric person authentication,expression analy-sis,and model-based image coding.Detecting and tracking faces in video sequences is a challenging task because faces are non-rigid and their images have a high degree of vari-ability in shape,texture,pose,and imaging conditions.De-tecting faces in images has received much attention.A com-prehensive survey on face detection methods can be found in[11].A huge research effort has been devoted to detecting and tracking of facial features in2D and3D.Many meth-ods have been proposed to detect facial features such as the eyes,the mouth,and the face contour.In the last two decades,some researchers have addressed the problem of face tracking by using model-based image coding paradigm[8].This paradigm wasfirst presented in1983as a possible basis of the next generation of vi-sual telecommunication due to itsflexibility and efficiency.Model-based image coding schemes can be divided into two types.One uses explicit face model to analyze moving fa-cial images(e.g,see[8]).In the second type,there is no explicit face model.For example,in[9],deformable2D shapes are represented using Point Distribution Models.This paper describes how we use an active model to adapt a deformable wireframe model to the face in each input image,i.e.to track the face through an image se-quence.Central to our approach is the decoupling of global head movements and local non-rigid deformations.This de-coupling is achieved by,first,estimating the global(rigid) motion using robust statistics and a statistical face texture model,and then,a local adaptation stage will adapt the3D model to possible local animation.This paper is organized as follows.Section2introduces our active model.This model is parameterized in geome-try and texture separately.Section3describes our global adaptation method.Section4describes our local adaptation method based on the concept of active appearance model search.Section5presents some experimental results.2.An active model2.1.A parameterized3D face modelBuilding a generic3D face model is a challenging task. Indeed,such a model should account for the differences be-tween different specific human faces as well as between dif-ferent facial expressions.This modeling was explored in the computer graphics,computer vision,and model-based im-age coding communities(e.g.,see[4]).In our study,we use the3D face model Candide[1].This3D wireframe model wasfirst developed at Link¨o ping University for the purpose of model-based image coding and computer anima-tion.The3D shape is directly recorded in coordinate form. The shape is given by a set of vertices and triangles.The 3D face model is given by the3D coordinates of the ver-tices P where is the number of vertices.Thus, the shape up to a global scale can be fully described by the -vector g–the concatenation of the3D coordinates of allvertices P.The vector g can be written as:gg is the standard shape of the model,and the columns of S and A are the Shape and Animation Units,respec-tively.Thus,the term S accounts for shape variability (inter-person variability)while the term A accounts for the facial animation(intra-person variability).The num-ber of columns of these matrices is given by the number of modes being used.In this study,we use12modes for the Shape Units matrix and six modes for the Animation Units matrix.As Animation Units,the following Action Units have been chosen:1)Jaw drop,2)Lip stretcher,3)Lip cor-ner depressor,4)Upper lip raiser,5)Eyebrow lowerer,6) Outer eyebrow raiser.The size of Candide model is limited to113vertices and183triangles allowing a low computa-tional cost.Sincerr.2.3.A parameterized texture modelA face texture is represented as a geometrically normal-ized image.The geometry of this image is obtained by pro-jecting the standard shapeFigure2.Two input images(top)and their ge-ometrically normalized image(bottom).Using Principal Component Analysis(PCA),the texture of any geometrically normalized image is a linear combina-tion of a set of texture modes or geometrically normalized eigenfaces[10].Thus,a texture x is given by:xx is the mean texture,the columns of X are the Tex-ture Units and is the vector of texture parameters.The PCA elements,x X X xreliable and fast by adopting a multi-stage scheme.First, three features are matched in the images and(the two inner eye corners and the philtrum top)from which a 2D affine transform is computed between and.Sec-ond,the2D features p are then matched in using a small search window centered on their2D affine transform.3.1.A RANSAC-based techniqueRetrieving the projection matrix M from the obtained set of putative3D-to-2D correspondences is carried out using two steps.Thefirst step explores the set of3D-to-2D correspondences using the conventional RANSAC paradigm[7].The second step selects the solution accord-ing to a consensus measure and the texture consistency with the statistical model.The goals of these two steps are:(i)removing possible mismatches and locally de-formed features from the computation of the projection matrix,(ii)preventing the model-based tracker from drifting-a common problem in face tracking.The proposed technique can be summarized as follows.Let be the total number of the putative3D-to-2D correspondences. For the sake of simplicity the subscript has been omitted. Exploration step:random samplingRandomly sample four3D-to-2D feature correspon-dences P p(non-coplanar configuration).The im-age points of this sample are chosen such that the mu-tual distance is large enough.Compute the projection matrix M using this sample.For all feature correspondences,compute the distance between the image features p and the projection M P.Count the number of features for which the distance is below some threshold.This number is denoted by.A threshold value between1.0and2.0pixels workswell for our system.Search step:consensus and texture consistencySort the projection matrices according to their in a descending order.For the best solutions(e.g.,5or10solutions),refit the matrix M using its inliers.For each such solution,compute the residual error be-tween the remapped texture and its PCA approxima-tion(Eq.(4)).Select the M which has the smallest residual error.Retrieve the3D pose parameters from M.In our experiments,the number of random samples iscapped at the number of feature correspondences which is variable since matches arefiltered out by thresholdingtheir normalized cross-correlation.Typically,is between70and100features.4.Local adaptationOnce the global adaptation is recovered,i.e.the projec-tion matrix M(or equivalently the vector z)is known,we aim at estimating the vector of animation parameters.Tothis end,we use the concept of the active appearance modelsearch[5,6].We proceed as follows.For a starting value of,supposed to be close to the optimum,we compute theresidual image r and according to Eq.(4),and find an update vector by multiplying the residual im-age with an update matrix(the pseudo-inverse of the gradi-ent matrix):U rWe compute a new parameter vector and a new error:andIf,we update accordingly and iterate until con-vergence.If,we try small update steps.Convergence is declared when the error cannot be improved anymore. The update matrix U is created in advance by training from example images with models correctly adapted.5.Experimental resultsTo try out the proposed scheme,the active model has been adapted to330images of six different persons from different angles and with different facial expressions.Then, the proposed adaptation algorithm has been applied to sev-eral test sequences.Figure3displays the adaptation to an input image at several stages of the proposed method.(a) displays the global adaptation result.(b)displays the local adaptation result obtained at the third iteration of the active appearance model search,and(c)the local adaptation at the sixth iteration(convergence).Note that the3D model has been correctly adapted to the head motion using the global adaptation stage while the mouth animation(local motion) was computed by the iterative search.Figure4shows the adaptation results associated with two video sequences(three frames are shown for each se-quence).The non-optimized implementation of the pro-posed adaptation takes about35ms including the matching (15ms),the global and local adaptation(20ms).Figure5 displays the adaptation results associated with the same in-put image using two different methods.The left image shows the simultaneous estimation of the global and localparameters using the active appearance model search de-scribed in[2],while the right image shows the computed adaptation using our developed decoupled adaptation.6.ConclusionWe have presented a new method for real-time3D face model adaptation.The proposed adaptation consists of two stages.In thefirst stage,the global adaptation(3D pose) was computed using a RANSAC-based technique integrat-ing the consensus measure and the texture consistency.In the second stage,the local adaptation(local animation)was computed using an active appearance model search.It has been shown that the developed framework has several ad-vantages since the strengths of complementary methodolo-gies are combined.Moreover,the proposed method can out-perform the directed search in which the global and local parameters are simultaneously estimated.Future work may investigate the use of the Kalmanfilter in order to smooth the estimates.References[1]J.Ahlberg.CANDIDE-3-an updated parametrized face.Technical Report LiTH-ISY-R-2326,Department of Electri-cal Engineering,Link¨o ping University,Sweden,2001. [2]J.Ahlberg.An active model for facial feature track-ing.EURASIP Journal on Applied Signal Processing, 2002(6):566–571,June2002.[3]Y.Aloimonos.Perspective approximations.Image and Vi-sion Computing,8(3):177–192,1990.[4]V.Blanz and T.Vetter.A morphable model for the synthesisof3D faces.In SIGGRAPH’99Conference,1999.[5]T.Cootes,G.Edwards,and C.Taylor.Active appearancemodels.In European Conference on Computer Vision,pages 484–498,1998.[6]T.Cootes,G.Edwards,and C.Taylor.Active appearancemodels.IEEE Transactions on Pattern Analysis and Ma-chine Intelligence,23(6):681–684,2001.[7]M.Fischler and R.Bolles.Random sample consensus:A paradigm for modelfitting with applications to imageanalysis and automated munication ACM, 24(6):381–395,1981.[8]H.Li,P.Roivainen,and R.Forchheimer.3D motion estima-tion in model-based image coding.IEEE Trans.on Pattern Analysis and Machine Intelligence,15(6):545–555,1993.[9]S.McKenna,S.Gong,R.Wurtz,J.Tanner,and D.Banin.Tracking facial feature points with Gabor wavelets and shape models.In Int.Conference on Audio-and Video-based Biometric Person Authentication,1997.[10]M.Turk and A.Pentland.Eigenfaces for recognition.Jour-nal of Cognitive Neuroscience,3(1),1991.[11]M.Yang,D.Kriegman,and N.Ahuja.Detecting faces inimages:A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence,24(1):34–58,2002.(a)(b)(c) Figure3.The adaptation process applied to an input image at different stages.(a)Global adaptation.(b)Local adaptation(first itera-tion).(c)Local adaptation(convergence).Figure4.The active model adapted to two videosequences.Figure5.A comparison between two adap-tation methods:Simultaneous estimation of the global and local parameters using active appearance model search(left);Our adapta-tion method(right).。

人脸识别外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)译文：基于PAC的实时人脸检测和跟踪方法摘要：这篇文章提出了复杂背景条件下，实现实时人脸检测和跟踪的一种方法。

这种方法是以主要成分分析技术为基础的。

为了实现人脸的检测，首先，我们要用一个肤色模型和一些动作信息(如：姿势、手势、眼色)。

然后，使用PAC技术检测这些被检验的区域，从而判定人脸真正的位置。

而人脸跟踪基于欧几里德（Euclidian）距离的，其中欧几里德距离在位于以前被跟踪的人脸和最近被检测的人脸之间的特征空间中。

用于人脸跟踪的摄像控制器以这样的方法工作：利用平衡/（pan/tilt）平台，把被检测的人脸区域控制在屏幕的中央。

这个方法还可以扩展到其他的系统中去，例如电信会议、入侵者检查系统等等。

1.引言视频信号处理有许多应用，例如鉴于通讯可视化的电信会议，为残疾人服务的唇读系统。

在上面提到的许多系统中，人脸的检测喝跟踪视必不可缺的组成部分。

在本文中，涉及到一些实时的人脸区域跟踪[1-3]。

一般来说，根据跟踪角度的不同，可以把跟踪方法分为两类。

有一部分人把人脸跟踪分为基于识别的跟踪喝基于动作的跟踪，而其他一部分人则把人脸跟踪分为基于边缘的跟踪和基于区域的跟踪[4]。

基于识别的跟踪是真正地以对象识别技术为基础的，而跟踪系统的性能是受到识别方法的效率的限制。

基于动作的跟踪是依赖于动作检测技术，且该技术可以被分成视频流（optical flow）的（检测）方法和动作—能量（motion－energy）的（检测）方法。

基于边缘的（跟踪）方法用于跟踪一幅图像序列的边缘，而这些边缘通常是主要对象的边界线。

然而，因为被跟踪的对象必须在色彩和光照条件下显示出明显的边缘变化，所以这些方法会遭遇到彩色和光照的变化。

此外，当一幅图像的背景有很明显的边缘时，（跟踪方法）很难提供可靠的（跟踪）结果。

当前很多的文献都涉及到的这类方法时源于Kass et al.在蛇形汇率波动[5]的成就。

面部识别技术对人类的影响英语作文The Impact of Facial Recognition Technology on Humanity.Facial recognition technology, a subset of biometric identification, has gained immense popularity in recent years, thanks to its accuracy, convenience, and widespread applicability. Its use cases range from unlocking smartphones to identifying criminal suspects in law enforcement agencies. However, this rapid advancement in technology has also sparked debates about its ethical implications and potential misuse. In this article, we will explore the pros and cons of facial recognition technology and its overall impact on humanity.Convenience and Security.The most obvious benefit of facial recognition technology is its convenience. Gone are the days when wehad to remember complex passwords or fumble with physical keys. With a simple scan of our faces, we can unlock ourphones, access secure areas, or even make payments. This ease of use has made our lives significantly more convenient.Moreover, facial recognition has also增强了安全性。