外文文獻(xiàn)翻譯成品：基于人臉識(shí)別的移動(dòng)自動(dòng)課堂考勤管理系統(tǒng)(中英文雙語對照)

外文標(biāo)題：FaceRecognition-BasedMobileAutomaticClassroomAttendanceManagementSystem外文作者：RefikSamet,MuhammedTanriverdi文獻(xiàn)出處:2018InternationalConferenceonCyberworlds(如覺得年份太老，可改為近2年，畢竟很多畢業(yè)生都這樣做)英文2937單詞，20013字符(字符就是印刷符)，中文4819漢字。FaceRecognition-BasedMobileAutomaticClassroomAttendanceManagementSystemAbstract—Classroomattendancecheckisacontributingfactortostudentparticipationandthefinalsuccessinthecourses.Takingattendancebycallingoutnamesorpassingaroundanattendancesheetarebothtime-consuming,andespeciallythelatterisopentoeasyfraud.Asanalternative,RFID,wireless,fingerprint,andirisandfacerecognition-basedmethodshavebeentestedanddevelopedforthispurpose.Althoughthesemethodshavesomepros,highsysteminstallationcostsarethemaindisadvantage.Thepresentpaperaimstoproposeafacerecognition-basedmobileautomaticclassroomattendancemanagementsystemneedingnoextraequipment.Tothisend,afilteringsystembasedonEuclideandistancescalculatedbythreefacerecognitiontechniques,namelyEigenfaces,FisherfacesandLocalBinaryPattern,hasbeendevelopedforfacerecognition.Theproposedsystemincludesthreedifferentmobileapplicationsforteachers,students,andparentstobeinstalledontheirsmartphonestomanageandperformthereal-timeattendance-takingprocess.TheproposedsystemwastestedamongstudentsatAnkaraUniversity,andtheresultsobtainedwereverysatisfactory.Keywords—facedetection,facerecognition,eigenfaces,fisherfaces,localbinarypattern,attendancemanagementsystem,mobileapplication,accuracyI.INTRODUCTIONMosteducationalinstitutionsareconcernedwithparticipationincoursessincestudentparticipationintheclassroomleadstoeffectivelearningandincreasessuccessrates[1].Also,ahighparticipationrateintheclassroomisamotivatingfactorforteachersandcontributestoasuitableenvironmentformorewillingandinformativeteaching[2].Themostcommonpracticeknowntoincreaseattendanceinacourseistakingattendanceregularly.Therearetwocommonwaystocreateattendancedata.Someteachersprefertocallnamesandputmarksforabsenceorpresence.Otherteachersprefertopassaroundapapersigningsheet.Aftergatheringtheattendancedataviaeitherofthesetwomethods,teachersmanuallyenterthedataintotheexistingsystem.However,thosenon-technologicalmethodsarenotefficientwayssincetheyaretime-consumingandpronetomistakes/fraud.Thepresentpaperaimstoproposeanattendance-takingprocessviatheexistingtechnologicalinfrastructurewithsomeimprovements.Afacerecognition-basedmobileautomaticclassroomattendancemanagementsystemhasbeenproposedwithafacerecognitioninfrastructureallowingtheuseofsmartmobiledevices.Inthisscope,afilteringsystembasedonEuclideandistancescalculatedbythreefacerecognitiontechniques,namelyEigenfaces,Fisherfaces,andLocalBinaryPattern(LBP),hasbeendevelopedforfacerecognition.Theproposedsystemincludesthreedifferentapplicationsforteachers,students,andparentstobeinstalledontheirsmartphonestomanageandperformareal-timepollingprocess,datatracking,andreporting.Thedataisstoredinacloudserverandaccessiblefromeverywhereatanytime.Webservicesareapopularwayofcommunicationforonlinesystems,andRESTfulisanoptimalexampleofwebservicesformobileonlinesystems[3].Intheproposedsystem,RESTfulwebserviceswereusedforcommunicationamongteacher,student,andparentapplicationsandthecloudserver.Attendanceresultsarestoredinadatabaseandaccessiblebytheteacher,studentandparentmobileapplications.Thepaperisorganisedasfollows.SectionIIprovidesabriefliteraturesurvey.SectionIIIintroducestheproposedsystem,andsectionIVfollowsbyimplementationandresults.Thelastsectiongivesthemainconclusions.LITERATURESURVEYFingerprintreadingsystemshavehighinstallationcosts.Furthermore,onlyonestudentatatimecanuseaportablefingerrecognitiondevice,whichmakesitatime-consumingprocess[4].Inthecaseofafixedfingerrecognitiondeviceattheentranceoftheclassroom,attendance-takingshouldbedoneundertheteacher'ssupervisionsothatstudentsdonotleaveafterthefingerrecognition,whichmakestheprocesstime-consumingforboththeteacherandthestudents.IncaseofRFIDcardreadingsystems,attendance-takingisavailableviathecardsdistributedtostudents[5].Insuchsystems,studentsmayresorttofraudulentmethodsbyreadingtheirfriends'cards.Also,ifastudentforgetshis/hercard,anon-trueabsencemaybesavedinthesystem.ThedisadvantageoftheclassroomscanningsystemswithBluetoothorbeaconmethodsisthateachstudentmustcarryadevice.BecausethefieldlimitoftheBluetoothLowEnergy(BLE)systemcannotbedetermined,studentswhoarenotintheclassroomatthemomentbutarewithintheBluetootharealimitsmayappeartobepresentintheattendancesystem[6].Therearedifferentmethodsofclassroomattendancemonitoringusingfacerecognitiontechnology.Oneoftheseisacameraplacedattheclassroomentranceandthestudentsenteringtheclassroomareregisteredintothesystembyfacerecognition[7].However,inthissystemstudents’facescouldberecognised,althoughstudentscanleavetheclassroomafterwards,anderrorscanoccurinthepollinginformation.Anothermethodistheobservationcarriedoutwithacameraplacedintheclassroomandtheclassroomimagetakenduringthecourse.Inthiscase,thecamerasusedinthesystemneedtobechangedfrequentlytokeepproducingbetterqualityimages.Therefore,thissystemisnotveryusefulandcanbecomecostly.Inadditiontoalltheaforementioneddisadvantages,themostcommondisadvantageisthatallthesemethodsneedextraequipment.Theproposedsystemhasbeendevelopedtoaddressthesedisadvantages.Themainadvantagesoftheproposedsystemareflexibleusage,noequipmentcosts,nowastedtime,andeasyaccessibility.PROPOSEDSYSTEMArchitectureoftheProposedSystemTheproposedsystem'sarchitecturebasedonmobilityandflexibilityisshowninFig.1.Figure1.SystemArchitectureThesystemconsistsofthreelayers:ApplicationLayer,CommunicationLayer,andServerLayer.ApplicationLayer:Intheapplicationlayer,therearethreemobileapplicationsconnectedtothecloudserverbywebservices.a)TeacherApplication:Theteacheristheheadofthesystem,sohe/shehastheprivilegetoaccessallthedata.Byhis/hersmartmobiledevice,he/shecantakeaphotoofstudentsinaclassroomatanytime.Afterthetakingthephotograph,theteachercanusethisphototoregisterattendance.Forthisaim,thephotoissenttothecloudserverforfacedetectionandrecognitionprocessing.Theresultsaresavedintoadatabasetogetherwithallthereachabledata.Theteachergetsaresponsebythemobileapplicationandcanimmediatelyseetheresults.Theteachercanalsocreateastudentprofile,addaphotoofeachstudent,andaddorremoveastudentto/fromtheirclassrosters.He/shecanaswellcreateanddeletecourses.Eachcoursehasauniquesix-charactercode.Theteachercansharethiscodewithhis/herstudentssotheycanaccesstheirattendanceresultsviathestudentapplication.Theteachercanaccesstoalldataandresultsbasedoneachstudent'srecognizedphotostampedwithadate.Additionally,anemailmessagewithattendancedataofaclassinExcelformatcanberequested,whiletheanalyticsoftheattendanceresultsisprovidedintheapplication.b)StudentApplication:Studentscansignincourseswiththeteacher'semailaddressandthesix-charactercoursecode.Theycanaddtheirphotosbytakingaphotoora3-secondlongvideo.Incaseoferrors,theiruploadedphotoscanbedeleted.Studentscanonlyseelimitedresultsoftheattendance-takingprocessrelatedtotheirattendance.Toprotectpersonalprivacy,theclassphotosanddetectedportraitphotosofeachstudentcanbeaccessedonlybytheteacher.Ifstudentsarenotintheclassroomwhenanattendance-checkisperformed,theyarenotifiedoftheattendance-check.Incaseoferrors(ifastudentispresent,butnotdetectedbythesystem),he/shecannotifytheteachersohe/shecanfixtheproblem.c)FamilyApplication:Parentscanseetheirchildren'sattendanceresultsforeachclass.Additionalchildrenprofilescanbeaddedintothesystem.Eachparentisaddedtothestudent'sapplicationwithname,surname,andemailaddress.Whenastudentaddshis/herparents,theyareautomaticallyabletoseetheattendanceresults.Theyarealsonotifiedwhentheirchildisnotintheclassroom.CommunicationLayer:RESTfulwebservicesareusedtocommunicatebetweentheapplicationsandserverlayers.RequestsaresentbythePOSTmethod.EachrequestissentwithauniqueIDoftheauthoriseduserofthesession.Onlytheauthoriseduserscanaccessandrespondthethedatatowhichtheyhaverighttoaccess.Duetoitsflexibilityandfastperformance,JSONisusedasthedataformatforwebservicesresponse[8].Withthisabstractwebservicelayer,thesystemcaneasilybeusedforanewitemintheapplicationlayer,suchaswebpagesoranewmobileoperatingsystem.ServerLayer:Theserverlayerisresponsibleforhandlingtherequestsandsendingtheresultstotheclient.Facedetectionandrecognitionalgorithmsareperformedinthislayerandmorethan30differentwebservicesarecreatedforhandlingdifferentrequestsfrommobileapplications.FaceDetectionAccurateandefficientfacedetectionalgorithmsimprovetheaccuracylevelofthefacerecognitionsystems.Ifafaceisnotdetectedcorrectly,thesystemwillfailitsoperation,stopprocessing,andrestart.Knowledge-based,feature-based,template-based,andstatistics-basedmethodsareusedforfacedetection[9].Sincetheclassroomphotoistakenundertheteacher'scontrol,posevariationscouldbelimitedtoasmallrange.Viola-JonesfacedetectionmethodwithAda-boosttrainingisshownasthebestchoiceforreal-timeclassattendancesystems[9,10].Inthemostbasicsense,thedesiredobjectsarefirstlyfoundandintroducedaccordingtoacertainalgorithm.Afterwards,theyarescannedtofindmatcheswithsimilarshapes[11].FaceRecognitionTherearetwobasicclassificationsoffacerecognitionbasedonimageintensity:feature-basedandappearance-based[12].Feature-basedapproachestrytorepresent(approximate)theobjectascompilationsofdifferentfeatures,forexample,eyes,nose,chin,etc.Incontrast,theappearance-basedmodelsonlyusetheappearancecapturedbydifferenttwo-dimensionalviewsoftheobject-of-interest.Feature-basedtechniquesaremoretime-consumingthanappearance-basedtechniques.Thereal-timeattendancemanagementsystemrequireslowcomputationalprocesstime.Therefore,threeappearance-basedfacerecognitiontechniquessuchasEigenfaces,FisherfacesandLBPareusedinthetestedsystem.Fisherfacesandeigenfacestechniqueshaveavaryingsuccessrate,dependingondifferentchallenges,likeposevariation,illumination,orfacialexpression[13].Accordingtoseveralpreviousstudies,facerecognitionusingLBPmethodgivesverygoodresultsregardingspeedanddiscriminationperformanceaswellasindifferentlightingconditions[14,15].Euclideandistanceiscalculatedbyfindingsimilaritiesbetweenimagesforfacerecognition.AfilteringsystembasedonEuclideandistancescalculatedbyEigenfaces,FisherfacesandLBPhasbeendevelopedforfacerecognition.Accordingtothed,,mnsf,sandEigenfacesalgorithmsareevaluatedindefinedorder.IftheEuclideandistanceofLBPalgorithmislessthan40;elseifEuclideandistanceofFisherfacesalgorithmislessthan250;elseifEuclideandistanceofEigenfacesalgorithmislessthan1500,recognizedfaceisrecordedastherightmatch.Secondly,ifthecalculatedEuclideandistancesbythethreemethodsaregreaterthantheminimumEuclideandistances,thesecondlevelEuclideandistances(40-50(forLBP),250-400(forFisherfaces),1500-1800(forEigenfaces))areevaluatedinthesameway.Ifthesecondlevelconditionsarealsonotmet,thefilterreturnsthewrongmatch.Thirdly,ifanytwoalgorithmsgivethesamematchresult,thematchisrecordedcorrectly.Finally,ifnoconditionsaremet,thepriorityisgiventotheLBPalgorithmandthematchisrecordedcorrectly.escarchitectureaimedr,,andtrequiringnoextraequipment.Atthesametime,itsobjectivewastoprovideaccesstoallusersatanytime.Thesystemthusoffersareal-timeattendancemanagementsystemtoallitsusers.IV.IMPLEMENTATIONANDRESULTSThefollowingplatformwasused.Thecloudserverhasa2.5GHzwith4-coreCPU,8GBRAM,and64-bitoperatingsystemcapacity.Viola-JonesfacedetectionalgorithmandEigenfaces,FisherfacesandLBPfacerecognitionalgorithmswereimplementedbasedonOpenCV.TestsweredonewithbothiOSandANDROID.Fortydifferentattendancemonitoringtestswereperformedinarealclassroom,including11students,and264students’facesweredetected.TablesI,II,andIIIshowdetectionandrecognitionaccuracyofallthreedifferenttypesoftestedalgorithmsrelatedtotheEuclideandistance.Priorityorderingfor3algorithmswasarrangedaccordingtoaccuracyrateforeachinterval.Intestresults,123,89,and85falserecognitionsweredetectedforEigenfaces,Fisherfacesandrespectively.Bythehelpofthedevelopedfilteringsystem,thenumberoffalserecognitionsdecreasedto65.Outof40implementedattendancemonitoringtests,10wereconductedwith1facephotoofeachstudentindatabaseinStep-I,20wereconductedwhenthenumberoffacephotosincreasedupto3inStep-II,and10recognitionprocesseswereconductedwithmorethan3facephotosindatabaseinStep-III.TableIVshowstheobtainedresults.Themostimportantlimitationoftestedattendancemonitoringprocessisdecreasedsuccesswithincreasingdistancebetweenthecameraandstudents.Theresultsregardingstudentssittinginfrontseatsaremoreaccurateincomparisontoresultsregardingstudentssittingintheback.Secondly,theaccuracyratesmayhavedecreasedduetotheblurringcausedbyvibrationwhilethephotowastaken.Thirdly,insomecasesonepartofthestudent'sfacemaybecoveredbyanotherstudentsittinginfrontofhim/her,whichmayhamperasuccessfulfacerecognitionprocess.Sincetheclassroomphotosaretakeninuncontrolledenvironments,theilluminationandposecould,toalargeextent,affecttheaccuracyrate.Thedevelopedfilteringsystemminimizestheseeffects.Toincreaseaccuracy,posetolerantfacerecognitionapproachmayalsobeused[16,17].V.CONCLUSIONSThepresentpaperproposesaflexibleandreal-timefacerecognition-basedmobileattendancemanagementsystem.AfilteringsystembasedonEuclideandistancescalculatedbyEigenfaces,Fisherfaces,andLBPhasbeendeveloped.Theproposedsystemeliminatesthecostforextraequipment,minimizesattendance-takingtime,andallowsuserstoaccessthedataanytimeandanywhere.Smartdevicesareveryuser-friendlytoperformclassroomattendancemonitoring.Teachers,students,andparentscanusetheapplicationwithoutanyrestrictionsandinreal-time.Sincetheinternetconnectionspeedhasbeensteadilyincreasing,highquality,largerimagescanbesenttotheserver.Inaddition,processorcapacityoftheserversisalsoincreasingondailybasis.Withthesetechnologicaldevelopments,theaccuracyrateoftheproposedsystemwillalsobeincreased.Facerecognitioncouldbefurthertestedbyotherfacerecognitiontechniques,suchasSupportVectorMachine,HiddenMarkovModel,NeuralNetworks,etc.Additionally,detectionandrecognitionprocessescouldbeperformedonsmartdevicesoncetheirprocessorcapacityissufficientlyincreased.REFERENCES[1]L.Stanca,"TheEffectsofAttendanceonAcademicPerformance:PanelDataEvidenceforIntroductoryMicroeconomics,"J.Econ.Educ.,vol.37,no.3,pp.251–266,2006.PaniandKishore,"AbsenteeismandperformanceinaquantitativemoduleAquantileregressionanalysis,"JournalofAppliedResearchinHigherEducation,vol.8no.3,pp.376-389,2016.[3]U.Thakar,A.Tiwari,andS.Varma,"OnCompositionofSOAPBasedandRESTfulServices,"IEEE6thInt.ConferenceonAdvancedComputing(IACC),2016.BasheerandC.V.Raghu,"Fingerprintattendancesystemforclassroomneeds,"AnnualIEEEIndiaConference(INDICON),pp.433-438,2012.[5]S.Konatham,B.S.Chalasani,N.Kulkarni,andT.E.Taeib,―AttendancegeneratingsystemusingRFIDandGSM,‖IEEELongIslandSystems,ApplicationsandTechnologyConference(LISAT),2016.[6]S.Noguchi,M.Niibori,E.Zhou,andM.Kamada,"StudentAttendanceManagementSystemwithBluetoothLowEnergyBeaconandAndroidDevices,"18thInternationalConferenceonNetwork-BasedInformationSystems,pp.710-713,2015.[7]S.ChintalapatiandM.V.Raghunadh,―Automatedattendancemanagementsystembasedonfacerecognitionalgorithms,‖IEEEInt.ConferenceonComputationalIntelligenceandComputingResearch,2013.[8]G.Wang,"Improvi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shù)據(jù)的電子郵件消息，同時(shí)在應(yīng)用程序中提供出勤結(jié)果的分析。b）學(xué)生申請：學(xué)生可以使用教3秒長的視頻來添加照片。如果出現(xiàn)錯(cuò)誤，可以刪除他們上傳的照片。學(xué)生只能看片和檢測到的肖像照片只能由老師訪問。如果學(xué)生在進(jìn)行出勤檢查時(shí)不在教室，則會(huì)通知他們出勤檢查。如果出現(xiàn)錯(cuò)誤（如果學(xué)生在場，但系統(tǒng)未檢測到他/她可以通知教師，以便他她可以解決問題。家庭申請：家長可以看到孩子每節(jié)課的出勤率?？梢詫⑵渌优渲梦募砑拥较到y(tǒng)中。每個(gè)父母都會(huì)添加到她的父母時(shí)，他們會(huì)自動(dòng)查看出勤結(jié)果。當(dāng)他們的孩子不在教室時(shí)，他們也會(huì)收到通知。3）通信層：RESTfulWeb服務(wù)用于在應(yīng)用程序和服務(wù)器層之間進(jìn)行通信。請求由POST方法發(fā)送。每個(gè)請求都使用會(huì)話的授權(quán)用戶的唯一ID發(fā)送。只有授權(quán)用戶才能訪問和響應(yīng)他們有權(quán)訪問的數(shù)據(jù)。由于其靈活性和快速性能，JSON被用作Web服務(wù)響應(yīng)的數(shù)據(jù)格式[8]。利用這種抽象的Web服務(wù)層，系統(tǒng)可以很容易地用于應(yīng)用層中的新項(xiàng)目，例如網(wǎng)頁或新的移動(dòng)操作系統(tǒng)。3）部檢測和識(shí)別算法，并且創(chuàng)建了30多種不同的Web服務(wù)，用于處理來自移動(dòng)應(yīng)用程序的不同請求。人臉檢測準(zhǔn)確有效的人臉檢測算法提高了人臉識(shí)別系統(tǒng)的準(zhǔn)確度。如果未正確檢測到面部，[9]。由于教室照片是在教師的控制下拍攝的，因此姿勢變化可能僅限于小范圍。使用Ada-boost訓(xùn)練的Viola-Jones面部檢測方法被視為實(shí)時(shí)課堂考勤系統(tǒng)的最佳選擇[9,10]。在最基本的意義上，首先根據(jù)某種算法找到并引入所需的對象。然后，他們被掃描以找到具有相似形狀的匹配[11].人臉識(shí)別[12]。基于特征的方法試圖將對象表示（近似）基于特征的技術(shù)比基于外觀的技術(shù)更耗時(shí)。實(shí)時(shí)考勤管理系統(tǒng)需要較低的計(jì)算處理時(shí)間。因此，在測試系統(tǒng)中使用三種基于外觀的面部識(shí)別技術(shù)，例如Eigenfaces，F(xiàn)isherfaces和LBP。Fisherfaces和eigenfaces技術(shù)具有不同[13]。根據(jù)之前的幾項(xiàng)研究，使用LBP方法進(jìn)行人臉識(shí)別可以在速度和辨別性能以及不同的光照條件[14,15]。通過尋找用于面部識(shí)別的圖像之間的相似性來計(jì)算歐幾里德距離?；跉W幾里德距離的特征面，F(xiàn)isherfaces和LBP計(jì)算的濾LBP，F(xiàn)isherfaces和Eigenfaces算法的最小歐幾里德距離。如果LBP算法的歐幾里德距離小于40;否則，如果Fisherfaces算法的歐幾里德距離小于250;否則，如果Eigenfaces算法的歐幾里德距離小于1500，則識(shí)別的面部被記錄為正確的第二級歐幾里德距離（對于，（對于，（對于特征臉）是以同樣的方式評估。如果也未滿足第二級條件，則過濾器返回錯(cuò)誤的匹配。第三，如果任何兩個(gè)算法給出相同的匹配結(jié)果，則正確記錄匹配。最后，如果沒有滿足條件，則優(yōu)先考慮LBP架構(gòu)旨在通過不需要額外設(shè)備來實(shí)現(xiàn)靈活性，移動(dòng)性和低成本。同時(shí)，其目標(biāo)是隨時(shí)為所有用戶提供訪問權(quán)限。因此，該系統(tǒng)為所有用戶提供實(shí)時(shí)考勤管理系統(tǒng)。四、實(shí)施和結(jié)果使用以下平臺(tái)。云服務(wù)器具有2.5GHz，4核CPU，8GBRAM和64位操作系統(tǒng)容量。基于OpenCV實(shí)現(xiàn)了Viola-Jones人臉檢測算法和特征臉，F(xiàn)isherfaces和LBP人臉識(shí)別算法。iOS和ANDROID都進(jìn)行了測試。在真實(shí)的教室中進(jìn)行了40次不同的出勤監(jiān)測測試，包括11名學(xué)生，并檢測到264名學(xué)生的面部。表II和III顯示了與歐氏距離相關(guān)的所有三種不同類型的測試算法的檢測和識(shí)別準(zhǔn)確度。根據(jù)每個(gè)間隔的準(zhǔn)確率安排3種算法的優(yōu)先級排序。在測試結(jié)果中，分別檢測到特征臉，F(xiàn)isherfaces和LBP的123,89和85個(gè)錯(cuò)誤識(shí)別。在開發(fā)的過濾系統(tǒng)的幫助下，錯(cuò)誤識(shí)別的數(shù)量減少到65個(gè)。在40個(gè)實(shí)施的考勤監(jiān)測測試中，10個(gè)進(jìn)行了步驟I1張面部照片，當(dāng)數(shù)量為20時(shí)進(jìn)行在步驟II中，面部照片增加到3，并且在步驟III中在數(shù)據(jù)庫中使用超過3張面部10IV顯示了獲得的結(jié)果。測試考勤監(jiān)控過程中最重要的限制是隨著攝像機(jī)與學(xué)生之間距離的增加而降低成功率。關(guān)于坐在前排座位上的學(xué)生的結(jié)果與坐在后排的學(xué)生的結(jié)果相比更準(zhǔn)確。其次，由于在拍攝照片時(shí)由振動(dòng)引起的模糊，準(zhǔn)確率可能已經(jīng)降低。第三，在某些情況下，學(xué)生面部的一部分可能被坐在他/她面前的另一名學(xué)生所覆蓋，這可能妨礙成功的面部識(shí)別過程。由于教室照片是在不受控制的環(huán)境中拍攝的，因此照明和姿勢可能在很大程度上影響準(zhǔn)確率。開發(fā)的過濾系統(tǒng)使這些影響最小化。為了提高準(zhǔn)確性，也可以使用姿勢容忍的人臉識(shí)別方法[16,17]。五、結(jié)論本文提出了一種靈活，實(shí)時(shí)的基于人臉識(shí)別的移動(dòng)考勤管理系統(tǒng)。已經(jīng)開發(fā)了基于歐幾里德距離的過濾系統(tǒng)，該過濾系統(tǒng)由特征臉，F(xiàn)isherfaces和LBP計(jì)算。所提出的系統(tǒng)消除了額外設(shè)備的成本，最大限度地減少了考勤時(shí)間，并允許用戶隨時(shí)隨地訪問數(shù)據(jù)。智能設(shè)備非常易于用戶進(jìn)行課堂出勤監(jiān)控。教師，學(xué)生和家長可以毫無限制地實(shí)時(shí)使用該應(yīng)用程序。由于互聯(lián)網(wǎng)連接速度一直在穩(wěn)步增長，因此可以向服務(wù)器發(fā)送高質(zhì)量，大圖像。此外，服務(wù)器的處理器容量也在日益增加。通過這些技術(shù)發(fā)展，所提出的系統(tǒng)的準(zhǔn)確率也將提高。人臉識(shí)別可以通過其他人臉識(shí)別技術(shù)進(jìn)一步測試，例如支持向量機(jī)，隱馬爾可夫模型，神經(jīng)網(wǎng)絡(luò)等。另外，一旦處理器容量充分增加，就可以在智能設(shè)備上執(zhí)行檢測和識(shí)別過程。參考文獻(xiàn)[1]L.Stanca,"TheEffectsofAttendanceonAcademicPerformance:PanelDataEvidenceforIntroductoryMicroeconomics,"J.Econ.Educ.,vol.37,no.3,pp.266,2006.PaniandKishore,"AbsenteeismandperformanceinaquantitativemoduleAquantileregressionanalysis,"JournalofAppliedResearchinHigherEducation,vol.8no.3,pp.376-389,2016.[3]U.Thakar,A.Tiwari,andS.Varm