人脸识别文献翻译中英文
支持人脸识别的英语作文
支持人脸识别的英语作文英文回答：In our modern, tech-savvy world, face recognition technology has emerged as a transformative tool with immense potential. With its ability to accurately identify individuals, it promises to revolutionize various aspects of our lives. As someone who firmly believes in its benefits, I enthusiastically endorse the adoption of face recognition technology.From enhancing security measures to streamlining daily tasks, face recognition offers a plethora of advantages. In a world marred by threats and vulnerabilities, it plays a crucial role in safeguarding our physical and digital spaces. The technology enables swift and accurate identification of individuals, ensuring only authorized personnel gain access to sensitive areas or online platforms.Beyond security applications, face recognition also enhances convenience. Imagine walking into a store and having the system identify you, providing personalized recommendations based on your preferences. It eliminates the need for tedious password inputs or remembering multiple identification cards. In the healthcare sector, face recognition can expedite patient registration and improve treatment accuracy by accessing medical records instantly.Moreover, face recognition has the potential to foster inclusivity and bridge societal divides. By eliminating language barriers and accommodating individuals with disabilities, it ensures everyone has equal access to essential services. It empowers the visually impaired to navigate public spaces confidently and enables non-native speakers to communicate seamlessly.Additionally, face recognition technology can contribute to scientific research and innovation. It aids in identifying patterns and correlations within large datasets,leading to groundbreaking discoveries in fields such as medicine and genetics. By recognizing facial expressions and emotions, it can assist in understanding human behavior and developing therapies for mental health disorders.Of course, concerns regarding privacy and potential misuse must be addressed. However, with robust ethical frameworks and stringent regulations, we can harness the benefits of face recognition technology while safeguarding individual rights. It is essential to ensure responsible implementation and prevent unauthorized access to sensitive information.In conclusion, face recognition technology holds immense promise for transforming our lives. Its ability to enhance security, streamline tasks, foster inclusivity, and contribute to research makes it a valuable tool in our technological arsenal. By embracing its potential while addressing ethical considerations, we can unlock the transformative power of face recognition and shape a brighter future for all.中文回答：在我们这个科技发达的现代社会，人脸识别技术已经成为一种极具潜力的变革性工具。
人脸识别英文专业词汇教学提纲
人脸识别英文专业词汇gallery set参考图像集Probe set=test set测试图像集face renderingFacial Landmark Detection人脸特征点检测3D Morphable Model 3D形变模型AAM (Active Appearance Model)主动外观模型Aging modeling老化建模Aging simulation老化模拟Analysis by synthesis 综合分析Aperture stop孔径光标栏Appearance Feature表观特征Baseline基准系统Benchmarking 确定基准Bidirectional relighting 双向重光照Camera calibration摄像机标定（校正）Cascade of classifiers 级联分类器face detection 人脸检测Facial expression面部表情Depth of field 景深Edgelet 小边特征Eigen light-fields本征光场Eigenface特征脸Exposure time曝光时间Expression editing表情编辑Expression mapping表情映射Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化Eye localization,眼睛定位face image acquisition 人脸图像获取Face aging人脸老化Face alignment人脸对齐Face categorization人脸分类Frontal faces 正面人脸Face Identification人脸识别Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪Facial action coding system面部动作编码系统Facial aging面部老化Facial animation parameters脸部动画参数Facial expression analysis人脸表情分析Facial landmark面部特征点Facial Definition Parameters人脸定义参数Field of view视场Focal length焦距Geometric warping几何扭曲Street view街景Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型Light-field光场Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择Capture systems获取系统Frontal lighting正面光照Open-set identification开集识别Operating point操作点Person detection行人检测Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化Pose correction姿态校正Privacy concern隐私关注Privacy policies隐私策略Profile extraction轮廓提取Rigid transformation刚体变换Sequential importance sampling序贯重要性抽样Skin reflectance model,皮肤反射模型Specular reflectance镜面反射Stereo baseline 立体基线Super-resolution超分辨率Facial side-view面部侧视图Texture mapping纹理映射Texture pattern纹理模式Rama Chellappa读博计划：1.完成先前关于指纹细节点统计建模的相关工作。
图像识别中英文对照外文翻译文献
中英文对照外文翻译文献(文档含英文原文和中文翻译)Elastic image matchingAbstractOne fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NP-complete by reduction from 3-SAT, thus giving evidence that the known exponential time algorithms are justified, but approximation algorithms or simplifications are necessary.Keywords: Elastic image matching; Two-dimensional warping; NP-completeness 1. IntroductionIn image recognition, a common problem is to match two given images, e.g. when comparing an observed image to given references. In that pro-cess, elastic image matching, two-dimensional (2D-)warping (Uchida and Sakoe, 1998) or similar types of invariant methods (Keysers et al., 2000) can be used. For this purpose, we can define cost functions depending on the distortion introduced in the matching andsearch for the best matching with respect to a given cost function. In this paper, we show that it is an algorithmically hard problem to decide whether a matching between two images exists with costs below a given threshold. We show that the problem image matching is NP-complete by means of a reduction from 3-SAT, which is a common method of demonstrating a problem to be intrinsically hard (Garey and Johnson, 1979). This result shows the inherent computational difficulties in this type of image comparison, while interestingly the same problem is solvable for 1D sequences in polynomial time, e.g. the dynamic time warping problem in speech recognition (see e.g. Ney et al., 1992). This has the following implications: researchers who are interested in an exact solution to this problem cannot hope to find a polynomial time algorithm, unless P=NP. Furthermore, one can conclude that exponential time algorithms as presented and extended by Uchida and Sakoe (1998, 1999a,b, 2000a,b) may be justified for some image matching applications. On the other hand this shows that those interested in faster algorithms––e.g. for pattern recognition purposes––are right in searching for sub-optimal solutions. One method to do this is the restriction to local optimizations or linear approximations of global transformations as presented in (Keysers et al., 2000). Another possibility is to use heuristic approaches like simulated annealing or genetic algorithms to find an approximate solution. Furthermore, methods like beam search are promising candidates, as these are used successfully in speech recognition, although linguistic decoding is also an NP-complete problem (Casacuberta and de la Higuera, 1999). 2. Image matchingAmong the varieties of matching algorithms,we choose the one presented by Uchida and Sakoe(1998) as a starting point to formalize the problem image matching. Let the images be given as(without loss of generality) square grids of size M×M with gray values (respectively node labels)from a finite alphabet &={1，…，G}. To define thed：&×&→N , problem, two distance functions are needed,one acting on gray valuesg measuring the match in gray values, and one acting on displacement differences :Z×Z→N , measuring the distortion introduced by t he matching. For these distance ddfunctions we assume that they are monotonous functions (computable in polynomial time) of the commonly used squared Euclid-ean distance, i.ed g (g 1,g 2)=f 1(||g 1-g 2||²)and d d (z)=f 2(||z||²) monotonously increasing. Now we call the following optimization problem the image matching problem (let µ={1，…M} ).Instance: The pair( A ; B ) of two images A and B of size M×M .Solution: A mapping function f :µ×µ→µ×µ.Measure:c （A,B,f ）=),(),(j i f ij g B Ad ∑μμ⨯∈),(j i+∑⨯-⋅⋅⋅∈+-+μ}1,{1,),()))0,1(),(())0,1(),(((M j i d j i f j i f dμ⨯-⋅⋅⋅∈}1,{1,),(M j i +∑⋅⋅⋅⨯∈+-+1}-M ,{1,),()))1,0(),(())1,0(),(((μj i d j i f j i f d 1}-M ,{1,),(⋅⋅⋅⨯∈μj iGoal:min f c(A,B,f).In other words, the problem is to find the mapping from A onto B that minimizes the distance between the mapped gray values together with a measure for the distortion introduced by the mapping. Here, the distortion is measured by the deviation from the identity mapping in the two dimensions. The identity mapping fulfills f(i,j)=(i,j),and therefore ，f((i,j)+(x,y))=f(i,j)+(x,y)The corresponding decision problem is fixed by the followingQuestion:Given an instance of image matching and a cost c′, does there exist a ma pping f such that c(A,B,f)≤c′?In the definition of the problem some care must be taken concerning the distance functions. For example, if either one of the distance functions is a constant function, the problem is clearly in P (for d g constant, the minimum is given by the identity mapping and for d d constant, the minimum can be determined by sorting all possible matching for each pixel by gray value cost and mapping to one of the pixels with minimum cost). But these special cases are not those we are concerned with in image matching in general.We choose the matching problem of Uchida and Sakoe (1998) to complete the definition of the problem. Here, the mapping functions are restricted by continuity and monotonicity constraints: the deviations from the identity mapping may locally be at most one pixel (i.e. limited to the eight-neighborhood with squared Euclidean distance less than or equal to 2). This can be formalized in this approach bychoosing the functions f1,f2as e.g.f 1=id，f2(x)=step(x):=⎩⎨⎧.2,)10(,2,0>≤⋅xGxMM3. Reduction from 3-SAT3-SAT is a very well-known NP-complete problem (Garey and Johnson, 1979), where 3-SAT is defined as follows:Instance: Collection of clauses C={C1，···,CK} on a set of variables X={x1， (x)L}such that each ckconsists of 3 literals for k=1，···K .Each literal is a variable or the negation of a variable.Question:Is there a truth assignment for X which satisfies each clause ck, k=1，···K ?The dependency graph D（Ф）corresponding to an instance Ф of 3-SAT is defined to be the bipartite graph whose independent sets are formed by the set of clauses Cand the set of variables X .Two vert ices ck and x1are adjacent iff ckinvolvesx 1or-xL.Given any 3-SAT formula U, we show how to construct in polynomial time anequivalent image matching problem l（Ф）=（A(Ф),B(Ф)）; . The two images of l （Ф）are similar according to the cost function （i.e.f:c(A(Ф),B(Ф),f）≤0) iff the formulaФ is satisfiable. We perform the reduction from 3-SAT using the following steps:• From the formula Ф we construct the dependency graph D（Ф）.• The dependency graph D（Ф）is drawn in the plane.• The drawing of D（Ф）is refined to depict the logical behaviour of Ф , yielding two images（A(Ф),B(Ф)）.For this, we use three types of components: one component to represent variables of Ф , one component to represent clauses of Ф, and components which act as interfaces between the former two types. Before we give the formal reduction, we introduce these components.3.1. Basic componentsFor the reduction from 3-SAT we need five components from which we will construct the in-stances for image matching , given a Boolean formula in 3-DNF,respectively its graph. The five components are the building blocks needed for the graph drawing and will be introduced in the following, namely the representations of connectors,crossings, variables, and clauses. The connectors represent the edges and have two varieties, straight connectors and corner connectors. Each of the components consists of two parts, one for image A and one for image B , where blank pixels are considered to be of the‘background ’color.We will depict possible mappings in the following using arrows indicating the direction of displacement (where displacements within the eight-neighborhood of a pixel are the only cases considered). Blank squares represent mapping to the respective counterpart in the second image.For example, the following displacements of neighboring pixels can be used with zero cost:On the other hand, the following displacements result in costs greater than zero:Fig. 1 shows the first component, the straight connector component, which consists of a line of two different interchanging colors,here denoted by the two symbols◇and□. Given that the outside pixels are mapped to their respe ctive counterparts and the connector is continued infinitely, there are two possible ways in which the colored pixels can be mapped, namely to the left (i.e. f(2,j)=(2,j-1)) or to the right (i.e. f(2,j)=(2,j+1)),where the background pixels have different possibilities for the mapping, not influencing the main property of the connector. This property, which justifies the name ‘connector ’, is the following: It is not possible to find a mapping, which yields zero cost where the relative displacements of the connector pixels are not equal, i.e. one always has f(2,j)-(2,j)=f(2,j')-(2,j'),which can easily be observed by induction over j'.That is, given an initial displacement of one pixel (which will be ±1 in this context), the remaining end of the connector has the same displacement if overall costs of the mapping are zero. Given this property and the direction of a connector, which we define to be directed from variable to clause, wecan define the state of the connector as carrying the‘true’truth value, if the displacement is 1 pixel in the direction of the connector and as carrying the‘false’ truth value, if the displacement is -1 pixel in the direction of the connector. This property then ensures that the truth value transmitted by the connector cannot change at mappings of zero cost.Image A image Bmapping 1 mapping 2Fig. 1. The straight connector component with two possible zero cost mappings.For drawing of arbitrary graphs, clearly one also needs corners,which are represented in Fig. 2.By considering all possible displacements which guarantee overall cost zero, one can observe that the corner component also ensures the basic connector property. For example, consider the first depicted mapping, which has zero cost. On the other hand, the second mapping shows, that it is not possible to construct a zero cost mapping with both connectors‘leaving’the component. In that case, the pixel at the position marked‘? ’either has a conflict (that i s, introduces a cost greater than zero in the criterion function because of mapping mismatch) with the pixel above or to the right of it,if the same color is to be met and otherwise, a cost in the gray value mismatch term is introduced.image A image Bmapping 1 mapping 2Fig. 2. The corner connector component and two example mappings.Fig. 3 shows the variable component, in this case with two positive (to the left) and one negated output (to the right) leaving the component as connectors. Here, a fourth color is used, denoted by ·.This component has two possible mappings for thecolored pixels with zero cost, which map the vertical component of the source image to the left or the right vertical component in the target image, respectively. (In both cases the second vertical element in the target image is not a target of the mapping.) This ensures±1 pixel relative displacements at the entry to the connectors. This property again can be deducted by regarding all possible mappings of the two images.The property that follows (which is necessary for the use as variable) is that all zero cost mappings ensure that all positive connectors carry the same truth value,which is the opposite of the truth value for all the negated connectors. It is easy to see from this example how variable components for arbitrary numbers of positive and negated outputs can be constructed.image A image BImage C image DFig. 3. The variable component with two positive and one negated output and two possible mappings (for true and false truth value).Fig. 4 shows the most complex of the components, the clause component. This component consists of two parts. The first part is the horizontal connector with a 'bend' in it to the right.This part has the property that cost zero mappings are possible for all truth values of x and y with the exception of two 'false' values. This two input disjunction，can be extended to a three input dis-junction using the part in the lower left. If the z connector carries a 'false' truth value, this part can only be mapped one pixel downwards at zero cost.In that case the junction pixel (the fourth pixel in the third row) cannot be mapped upwards at zero cost and the 'two input clause' behaves as de-scribed above. On the other hand, if the z connector carries a 'true' truth value, this part can only be mapped one pixel upwards at zero cost,and the junction pixel can be mapped upwards,thus allowing both x and y to carry a 'false' truth value in a zero cost mapping. Thus there exists a zero cost mapping of the clause component iff at least one of the input connectors carries a truth value.image Aimage B mapping 1(true,true,false)mapping 2 (false,false,true,)Fig. 4. The clause component with three incoming connectors x, y , z and zero cost mappings forthe two cases(true,true,false)and (false, false, true).The described components are already sufficient to prove NP-completeness by reduction from planar 3-SAT (which is an NP-complete sub-problem of 3-SAT where the additional constraints on the instances is that the dependency graph is planar),but in order to derive a reduction from 3-SAT, we also include the possibility of crossing connectors.Fig. 5 shows the connector crossing, whose basic property is to allow zero cost mappings if the truth–values are consistently propagated. This is assured by a color change of the vertical connector and a 'flexible' middle part, which can be mapped to four different positions depending on the truth value distribution.image Aimage Bzero cost mappingFig. 5. The connector crossing component and one zero cost mapping.3.2. ReductionUsing the previously introduced components, we can now perform the reduction from 3-SAT to image matching .Proof of the claim that the image matching problem is NP-complete:Clearly, the image matching problem is in NP since, given a mapping f and two images A and B ,the computation of c（A,B,f）can be done in polynomial time. To prove NP-hardness, we construct a reduction from the 3-SAT problem. Given an instance of 3-SAT we construct two images A and B , for which a mapping of cost zero exists iff all the clauses can be satisfied.Given the dependency graph D ,we construct an embedding of the graph into a 2D pixel grid, placing the vertices on a large enough distance from each other (say100（K+L）² ).This can be done using well-known methods from graph drawing (see e.g.di Battista et al.,1999).From this image of the graph D we construct the two images A and B , using the components described above.Each vertex belonging to a variable is replaced with the respective parts of the variable component, having a number of leaving connectors equal to the number of incident edges under consideration of the positive or negative use in the respective clause. Each vertex belonging to a clause is replaced by the respective clause component,and each crossing of edges is replaced by the respective crossing component. Finally, all the edges are replaced with connectors and corner connectors, and the remaining pixels inside the rectangular hull of the construction are set to the background gray value. Clearly, the placement of the components can be done in such a way that all the components are at a large enough distance from each other, where the background pixels act as an 'insulation' against mapping of pixels, which do not belong to the same component. It can be easily seen, that the size of the constructed images is polynomial with respect to the number of vertices and edges of D and thus polynomial in the size of the instance of 3-SAT, at most in the order （K+L）².Furthermore, it can obviously be constructed in polynomial time, as the corresponding graph drawing algorithms are polynomial.Let there exist a truth assignment to the variables x1，…，xL, which satisfies allthe clauses c1，…，cK. We construct a mapping f , that satisfies c（f,A,B）=0 asfollows.For all pixels (i, j ) belonging to variable component l with A（i，j）not of the background color,set f（i，j）=（i，j-1）if xlis assigned the truth value 'true' , set f（i，j）=（i，j+1）, otherwise. For the remaining pixels of the variable component set A（i，j）=B（i，j），if f（i，j）=（i，j）, otherwise choose f（i，j）from{(i,j+1),(i+1,j+1),(i-1,j+1)}for xl'false' respectively from {(i,j-1),(i+1,j-1),(i-1,j-1)}for xl'true ',such that A（i，j）=B（f（i，j））. This assignment is always possible and has zero cost, as can be easily verified.For the pixels（i，j）belonging to (corner) connector components,the mapping function can only be extended in one way without the introduction of nonzero cost,starting from the connection with the variable component. This is ensured by thebasic connector property. By choosing f （i ，j ）=（i,j ）for all pixels of background color, we obtain a valid extension for the connectors. For the connector crossing components the extension is straight forward, although here ––as in the variable mapping ––some care must be taken with the assign ment of the background value pixels, but a zero cost assignment is always possible using the same scheme as presented for the variable mapping.It remains to be shown that the clause components can be mapped at zero cost, if at least one of the input connectors x , y , z carries a ' true' truth value.For a proof we regard alls even possibilities and construct a mapping for each case. In thedescription of the clause component it was already argued that this is possible,and due to space limitations we omit the formalization of the argument here.Finally, for all the pixels （i ，j ）not belonging to any of the components, we set f （i ，j ）=（i ，j ）thus arriving at a mapping function which has c （f ，A ，B ）=0。
人脸识别英文专业词汇
gallery set参考图像集Probe set=test set测试图像集face renderingFacial Landmark Detection人脸特征点检测3D Morphable Model 3D形变模型AAM (Active Appearance Model)主动外观模型Aging modeling老化建模Aging simulation老化模拟Analysis by synthesis 综合分析Aperture stop孔径光标栏Appearance Feature表观特征Baseline基准系统Benchmarking 确定基准Bidirectional relighting 双向重光照Camera calibration摄像机标定（校正）Cascade of classifiers 级联分类器face detection 人脸检测Facial expression面部表情Depth of field 景深Edgelet 小边特征Eigen light-fields本征光场Eigenface特征脸Exposure time曝光时间Expression editing表情编辑Expression mapping表情映射Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化Eye localization,眼睛定位face image acquisition 人脸图像获取Face aging人脸老化Face alignment人脸对齐Face categorization人脸分类Frontal faces 正面人脸Face Identification人脸识别Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪Facial action coding system面部动作编码系统Facial aging面部老化Facial animation parameters脸部动画参数Facial expression analysis人脸表情分析Facial landmark面部特征点Facial Definition Parameters人脸定义参数Field of view视场Focal length焦距Geometric warping几何扭曲Street view街景Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型Light-field光场Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择Capture systems获取系统Frontal lighting正面光照Open-set identification开集识别Operating point操作点Person detection行人检测Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化Pose correction姿态校正Privacy concern隐私关注Privacy policies隐私策略Profile extraction轮廓提取Rigid transformation刚体变换Sequential importance sampling序贯重要性抽样Skin reflectance model,皮肤反射模型Specular reflectance镜面反射Stereo baseline 立体基线Super-resolution超分辨率Facial side-view面部侧视图Texture mapping纹理映射Texture pattern纹理模式Rama Chellappa读博计划：完成先前关于指纹细节点统计建模的相关工作。
科技文献中英文对照翻译
Sensing Human Activity:GPS Tracking感应人类活动:GPS跟踪Stefan van der Spek1,*,Jeroen van Schaick1,Peter de Bois1,2and Remco de Haan1Abstract:The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools,but also as instruments used to capture travelled routes:assensors that measure activity on a city scale or the regional scale.TU Delft developed aprocess and database architecture for collecting data on pedestrian movement in threeEuropean city centres,Norwich,Rouen and Koblenz,and in another experiment forcollecting activity data of13families in Almere(The Netherlands)for one week.Thequestion posed in this paper is:what is the value of GPS as‘sensor technology’measuringactivities of people?The conclusion is that GPS offers a widely useable instrument tocollect invaluable spatial-temporal data on different scales and in different settings addingnew layers of knowledge to urban studies,but the use of GPS-technology and deploymentof GPS-devices still offers significant challenges for future research.摘要：增强GPS技术支持使用GPS设备不仅作为导航和定位工具,但也为仪器用来捕捉旅行路线:作为传感器,测量活动在一个城市或区域范围内规模。
外文文献翻译成品：基于人脸识别的移动自动课堂考勤管理系统(中英文双语对照)
外文标题：Face Recognition-Based Mobile Automatic Classroom Attendance Management System外文作者：Refik Samet,Muhammed Tanriverdi文献出处: 2018 International Conference on Cyberworlds (如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文2937单词，20013字符(字符就是印刷符)，中文4819汉字。
Face Recognition-Based Mobile Automatic ClassroomAttendance Management System Abstract—Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.Keywords—face detection, face recognition, eigenfaces, fisherfaces, local binary pattern, attendance management system, mobile application, accuracyI.INTRODUCTIONMost educational institutions are concerned with st udents’ participation in courses since student participation in the classroom leads to effective learning and increases success rates [1]. Also, a high participation rate in the classroom is a motivating factor for teachers and contributes to a suitable environment for more willing and informative teaching [2]. The most common practice known to increase attendance in a course is taking attendance regularly. There are two common ways to create attendance data. Some teachers prefer to call names and put marks for absence or presence. Other teachers prefer to pass around a paper signing sheet. After gathering the attendance data via either of these two methods, teachers manually enter the data into the existing system. However, those non-technological methods are not efficient ways since they are time- consuming and prone to mistakes/fraud. The present paper aims to propose an attendance-taking process via the existing technological infrastructure with some improvements. A face recognition-based mobile automatic classroom attendance management system has been proposed with a face recognition infrastructure allowing the use of smart mobile devices. In this scope, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces, and Local Binary Pattern (LBP), has been developedfor face recognition. The proposed system includes three different applications for teachers, students, and parents to be installed on their smart phones to manage and perform a real-time polling process, data tracking, and reporting. The data is stored in a cloud server and accessible from everywhere at any time. Web services are a popular way of communication for online systems, and RESTful is an optimal example of web services for mobile online systems [3]. In the proposed system, RESTful web services were used for communication among teacher, student, and parent applications and the cloud server. Attendance results are stored in a database and accessible by the teacher, student and parent mobile applications.The paper is organised as follows. Section II provides a brief literature survey. SectionIII introduces the proposed system, and section IV follows by implementation and results. The last section gives the main conclusions.II.LITERATURE SURVEYFingerprint reading systems have high installation costs. Furthermore, only one student at a time can use a portable finger recognition device, which makes it atime-consuming process [4]. In the case of a fixed finger recognition device at the entrance of the classroom, attendance-taking should be done under the teacher's supervision so that students do not leave after the finger recognition, which makes the process time-consuming for both the teacher and the students. In case of RFID card reading systems, attendance-taking is available via the cards distributed to students [5]. In such systems, students may resort to fraudulent methods by reading their friends' cards. Also, if a student forgets his/her card, a non- true absence may be saved in the system. The disadvantage of the classroom scanning systems with Bluetooth or beacon methods is that each student must carry a device. Because the field limit of the Bluetooth Low Energy (BLE) system cannot be determined, students who are not inthe classroom at the moment but are within the Bluetooth area limits may appear to be present in the attendance system [6]. There are different methods of classroom attendance monitoring using face recognition technology. One of these is a camera placed at the classroom entrance and the students entering the classroom are registered into the system by face recognition [7]. However, in this system students’faces could be recognised, although students can leave the classroom afterwards, and errors can occur in the polling information. Another method is the observation carried out with a camera placed in the classroom and the classroom image taken during the course. In this case, the cameras used in the system need to be changed frequently to keep producing better quality images. Therefore, this system is not very useful andcan become costly. In addition to all the aforementioned disadvantages, the most common disadvantage is that all these methods need extra equipment. The proposed system has been developed to address these disadvantages. The main advantages of the proposed system are flexible usage, no equipment costs, no wasted time, and easy accessibility.III.PROPOSED SYSTEMA.Architecture of the Proposed SystemThe proposed system's architecture based on mobility and flexibility is shown inFig.1.Figure 1. System ArchitectureThe system consists of three layers: Application Layer, Communication Layer, and Server Layer.Application Layer: In the application layer, there are three mobile applications connected to the cloud server by web services. a) Teacher Application: The teacher is the head of the system, so he/she has the privilege to access all the data. By his/her smart mobile device, he/she can take a photo of students in a classroom at any time. After the taking the photograph, the teacher can use this photo to register attendance. For this aim, the photo is sent to the cloud server for face detection and recognition processing. The results are saved into a database together with all the reachable data. The teacher gets a response by the mobile application and can immediately see the results. The teacher can also create a student profile, add a photo of each student, and add or remove a student to/from their class rosters. He/she can as well create and delete courses. Each course has a unique six- character code. The teacher can share this code with his/her students so they can access their attendance results via the student application. The teacher can access to all data and results based on each student's recognized photo stamped with a date. Additionally, an email message with attendance data of a class in Excel format can be requested, while the analytics of the attendance results is provided in the application. b) Student Application: Students can sign in courses with the teacher's email address and the six-character course code. They can add their photos by taking a photo or a 3-second long video. In case of errors, their uploaded photos can be deleted. Students can only see limited results of the attendance-taking process related to their attendance. To protect personal privacy, the class photos and detected portrait photos of each student can be accessed only by the teacher. If students are not in the classroom when an attendance-check is performed, they are notified of the attendance-check. In case of errors (if a student is present, but not detected by the system), he/she can notify the teacher so he/she can fix the problem. c) Family Application: Parents can see their children's attendanceresults for each class. Additional children profiles can be added into the system. Each parent is added to the student's application with name, surname, and email address. When a student adds his/her parents, they are automatically able to see the attendance results. They are also notified when their child is not in the classroom.2) Communication Layer: RESTful web services are used to communicate betweenthe applications and server layers. Requests are sent by the POST method. Each request is sent with a unique ID of the authorised user of the session. Only the authorised users can access and respond the the data to which they have right to access. Due to its flexibility and fast performance, JSON is used as the data format for web services response [8]. With this abstract web service layer, the system can easily be used for a new item in the application layer, such as web pages or a new mobile operating system.3)Server Layer: The server layer is responsible for handling the requests and sending the results to the client. Face detection and recognition algorithms are performed in this layer and more than 30 different web services are created for handling different requests from mobile applications.B.Face DetectionAccurate and efficient face detection algorithms improve the accuracy level of theface recognition systems. If a face is not detected correctly, the system will fail its operation, stop processing, and restart. Knowledge-based, feature-based,template-based, and statistics-based methods are used for face detection [9]. Since the classroom photo is taken under the teacher's control, pose variations could be limited to a small range. Viola-Jones face detection method with Ada- boost training is shown as the best choice for real-time class attendance systems [9, 10]. In the most basic sense, the desired objects are firstly found and introduced according to a certain algorithm. Afterwards, they are scanned to find matches with similar shapes [11].C.Face RecognitionThere are two basic classifications of face recognition based on image intensity: feature-based and appearance-based [12]. Feature-based approaches try to represent (approximate) the object as compilations of different features, for example, eyes, nose, chin, etc. In contrast, the appearance-based models only use the appearance captured by different two-dimensional views of the object-of-interest. Feature-based techniques are more time-consuming than appearance-based techniques. The real-time attendance management system requires low computational process time. Therefore, three appearance-based face recognition techniques such as Eigenfaces, Fisherfaces and LBP are used in the tested system. Fisherfaces and eigenfaces techniques have a varying success rate, depending on different challenges, like pose variation, illumination, or facial expression [13]. According to several previous studies, face recognition using LBP method gives very good results regarding speed and discrimination performance as well as in different lighting conditions [14, 15]. Euclidean distance is calculated by finding similarities between images for face recognition. A filtering system based on Euclidean distances calculated by Eigenfaces, Fisherfaces and LBP has been developed for face recognition. According to the developed system, firstly, minimum Euclidean distances of LBP, Fisherfaces andEigenfaces algorithms are evaluated in defined order. If the Euclidean distance of LBP algorithm is less than 40; else if Euclidean distance of Fisherfaces algorithm is less than 250; else if Euclidean distance of Eigenfaces algorithm is less than 1500, recognized face is recorded as the right match. Secondly, if the calculated Euclidean distances by the three methods are greater than the minimum Euclidean distances, the second level Euclidean distances (40-50 (for LBP), 250-400 (for Fisherfaces), 1500- 1800 (for Eigenfaces)) are evaluated in the same way. If the second level conditions are also not met, the filter returns the wrong match. Thirdly, if any two algorithms give the same match result, the match is recorded correctly. Finally, if no conditions are met, the priority is given to the LBP algorithm and the match is recorded correctly. The system’s specific architecture aimed for flexibility, mobility, and low-cost by requiring no extra equipment. At the same time, its objective was to provide access to all users at any time. The system thus offers a real-time attendance management system to all its users.IV.IMPLEMENTATION AND RESULTSThe following platform was used. The cloud server has a 2.5 GHz with 4-core CPU,8GB RAM, and 64-bit operating system capacity. Viola-Jones face detection algorithm and Eigenfaces, Fisherfaces and LBP face recognition algorithms were implemented based on OpenCV. Tests were done with both iOS and ANDROID.Forty different attendance monitoring tests were performed in a real classroom, including 11 students, and 264 students’ faces were detected. Tables I, II, and III show detection and recognition accuracy of all three different types of tested algorithms related to the Euclidean distance.Priority ordering for 3 algorithms was arranged according to accuracy rate for each interval. In test results, 123, 89, and 85 false recognitions were detected for Eigenfaces, Fisherfaces and LBP, respectively. By the help of the developed filtering system, the number of false recognitions decreased to 65. Out of 40 implemented attendance monitoring tests, 10 were conducted with 1 face photo of each student in database in Step-I, 20 were conducted when the number of face photos increased up to 3 in Step-II, and 10 recognition processes were conducted with more than 3 face photos in database in Step-III. Table IV shows the obtained results.The most important limitation of tested attendance monitoring process is decreased success with increasing distance between the camera and students. The results regarding students sitting in front seats are more accurate in comparison to results regarding students sitting in the back. Secondly, the accuracy rates may have decreased due to the blurring caused by vibration while the photo was taken. Thirdly, in some cases one part of the student's face may be covered by another student sitting in front of him/her, which may hamper a successful face recognition process. Since the classroom photos are taken in uncontrolled environments, the illumination and pose could, to a large extent, affect the accuracy rate. The developed filtering system minimizes these effects. To increase accuracy, pose tolerant face recognition approach may also be used [16, 17].V.CONCLUSIONSThe present paper proposes a flexible and real-time face recognition-based mobile attendance management system. A filtering system based on Euclidean distances calculated by Eigenfaces, Fisherfaces, and LBP has been developed. The proposed system eliminates the cost for extra equipment, minimizes attendance-taking time, and allows users to access the data anytime and anywhere. Smart devices are very user- friendly to perform classroom attendance monitoring. Teachers, students, and parents can use the application without any restrictions and in real-time. Since the internet connection speed has been steadily increasing, high quality, larger images can be sent to the server. In addition, processor capacity of the servers is also increasing on daily basis. With these technological developments, the accuracy rate of the proposed system will also be increased. Face recognition could be further tested by other face recognition techniques, such as Support Vector Machine, Hidden Markov Model, Neural Networks, etc. Additionally, detection and recognition processes could be performed on smart devices once their processor capacity is sufficiently increased. REFERENCES[1]L. Stanca, "The Effects of Attendance on Academic Performance:Panel Data Evidence for Introductory Microeconomics," J. Econ. Educ., vol. 37, no. 3, pp. 251–266, 2006.[2]P.K. Pani and P. Kishore, "Absenteeism and performance in a quantitative moduleA quantile regression analysis," Journal of Applied Research in Higher Education, vol.8 no. 3, pp. 376-389, 2016.[3]U. Thakar, A. Tiwari, and S. Varma, "On Composition of SOAP Based and RESTful Services," IEEE 6th Int. Conference on Advanced Computing (IACC), 2016. [4]K.P.M. Basheer and C.V. Raghu, "Fingerprint attendance system for classroom needs," Annual IEEE India Conference (INDICON), pp. 433-438, 2012.[5]S. Konatham, B.S. Chalasani, N. Kulkarni, and T.E. Taeib, ―Attendance generating system using RFID and GSM,‖IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2016.[6]S. Noguchi, M. Niibori, E. Zhou, and M. Kamada, "Student Attendance Management System with Bluetooth Low Energy Beacon and Android Devices," 18th International Conference on Network- Based Information Systems, pp. 710-713, 2015.[7]S. Chintalapati and M.V. Raghunadh, ―Automated attendance management system based on face recognition algorithms,‖IEEE Int. Conference on Computational Intelligence and Computing Research, 2013.[8]G. Wang, "Improving Data Transmission in Web Applications via the Translation between XML and JSON," Third Int. Conference on Communications and Mobile Computing (CMC), pp. 182-185, 2011.[9]X. Zhu, D. Ren, Z. Jing, L. Yan, and S. Lei, "Comparative Research of theCommon Face Detection Methods," 2nd International Conference on Computer Science and Network Technology, pp. 1528-1533, 2012.[10]V. Gupta and D. Sharma, ―A Study of Various Face Detection Methods,‖International Journal of Advanced Research in Computer and Communication Engineering vol. 3, no. 5, pp.6694-6697, 2014.[11]P. Viaola and M.J. Jones, ―Robust Real-Time Face Detection,‖International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.[12]L. Masupha, T. Zuva, S. Ngwira, and O. Esan, ―Face recognition techniques, their advantages, disadvantages and performance evaluation,‖Int. Conference on Computing, Communication and Security (ICCCS), 2015.[13]J. Li, S. Zhou, and C. Shekhar, "A comparison of subspace analysis for face recognition," International Conference on Multimedia and Expo, ICME '03, Proceedings, vol. 3, pp. 121-124, 2003.[14]T. Ahonen, A. Hadid, and M. Pietikainen, ―Face description with Local Binary Patterns,‖IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.[15]T. Ahonen, A. Hadid, M. Pietikainen, and T . Maenpaa, ―Face recognition based on the appearance of local regions,‖Proceedings of the 17th Int. Conference on Pattern Recognition, vol. 3, pp. 153-156, 2004.[16]R. Samet, S. Sakhi, and K. B. Baskurt, ―An Efficient Pose Tolerant Face Recognition Approach‖, Transactions on Comput. Science XXVI, LNCS 9550, pp.161-172, 2016.[17]R. Samet, G. S. Shokouh, J. Li, ―A Novel Pose Tolerant Face Recognition Approach‖, 2014 International Conference on Cyberworlds, pp. 308-312, 2014.基于人脸识别的移动自动课堂考勤管理系统摘要- 课堂出勤检查是学生参与和课程最终成功的一个因素。
人脸识别作文500字
人脸识别作文500字英文回答：Face recognition technology has become increasingly prevalent in our society. It is a powerful tool that can be used for various purposes, such as unlocking our smartphones, enhancing security systems, and even identifying criminals. This technology works by analyzing unique facial features and patterns, and then comparing them to a database of known faces. It has revolutionized the way we interact with technology and has the potential to greatly impact our daily lives.One of the main advantages of face recognition technology is its convenience. Gone are the days of remembering complex passwords or carrying around physical keys. With just a glance, we can unlock our devices or gain access to secure areas. This not only saves us time but also provides a higher level of security. For example, imagine being able to walk into a concert venue withouthaving to worry about losing your ticket or showing identification. Face recognition technology makes this possible by instantly verifying your identity.Another benefit of face recognition technology is its potential to enhance public safety. Law enforcement agencies can use this technology to quickly identify suspects and locate missing persons. For instance, if a child goes missing, their photograph can be uploaded into a facial recognition system, which can then scan surveillance footage from various locations to help locate the child. This can significantly increase the chances of finding the missing person and bringing them back to safety.However, like any technology, face recognition also raises concerns about privacy and security. Some argue that the widespread use of this technology could infringe upon our right to privacy. For instance, imagine walking down the street and having your every move tracked and recorded by facial recognition cameras. This could potentially lead to a surveillance state where our every action is monitored and analyzed. Additionally, there are concerns about thesecurity of the facial recognition databases themselves. If these databases were to be hacked or accessed by unauthorized individuals, it could lead to serious consequences, such as identity theft or impersonation.Despite these concerns, face recognition technology has the potential to greatly benefit society. It can streamline processes, improve security, and help solve crimes. However, it is important to strike a balance between the advantagesof this technology and the protection of individual privacy rights. Proper regulations and safeguards need to be put in place to ensure that face recognition technology is used responsibly and ethically.中文回答：人脸识别技术在我们的社会中越来越普遍。
人脸识别英文专业词汇
gallery set参考图像集Probe set=test set测试图像集face renderingFacial Landmark Detection人脸特征点检测3D Morphable Model 3D形变模型AAM (Active Appearance Model)主动外观模型Aging modeling老化建模Aging simulation老化模拟Analysis by synthesis 综合分析Aperture stop孔径光标栏Appearance Feature表观特征Baseline基准系统Benchmarking 确定基准Bidirectional relighting双向重光照Camera calibration摄像机标定（校正）Cascade of classifiers级联分类器face detection 人脸检测Facial expression面部表情Depth of field 景深Edgelet 小边特征Eigen light-fields本征光场Eigenface特征脸Exposure time曝光时间Expression editing表情编辑Expression mapping表情映射Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化Eye localization,眼睛定位face image acquisition人脸图像获取Face aging人脸老化Face alignment人脸对齐Face categorization人脸分类Frontal faces 正面人脸Face Identification人脸识别Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪Facial action coding system面部动作编码系统Facial aging面部老化Facial animation parameters脸部动画参数Facial expression analysis人脸表情分析Facial landmark面部特征点Facial Definition Parameters人脸定义参数Field of view视场Focal length焦距Geometric warping几何扭曲Street view街景Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型Light-field光场Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择Capture systems获取系统Frontal lighting正面光照Open-set identification开集识别Operating point操作点Person detection行人检测Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化Pose correction姿态校正Privacy concern隐私关注Privacy policies隐私策略Profile extraction轮廓提取Rigid transformation刚体变换Sequential importance sampling序贯重要性抽样Skin reflectance model,皮肤反射模型Specular reflectance镜面反射Stereo baseline立体基线Super-resolution超分辨率Facial side-view面部侧视图Texture mapping纹理映射Texture pattern纹理模式Rama Chellappa读博计划：1.完成先前关于指纹细节点统计建模的相关工作。