000			03274nam a22002777a 4500
003			KOHA
005			20221104110125.0
008			221104d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aD 330 _bM45 2022
100	1		_aMilad, Ali Ali Alhadar
245	1	0	_aAUTOMATIC CLASS ATTENDANCE MANAGEMENT SYSTEM USING FACIAL SYNTHESIS AND RECOGNITION / _cALI ASLI ALHADAR MILAD; SUPERVISOR: ASSOC. PROF. DR. KAMIL YURTKAN
264			_c2022
300			_a135 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department
504			_aIncludes bibliography (sheets 124-135)
520			_aABSTRACT Classroom attendance systems have been a principal part of the learning process of conventional classroom institutions. Many processes have been devised over the years for the adequate recording of student attendance in classrooms to ensure students attend their classes which is synonymous to good academic performance in students. The rise of the digital age in the 21st century has enabled the introduction of several electronic technological platforms for classroom attendance purposes, one of such technologies is the use of human face recognition for classroom attendance. Unfortunately classroom attendance has been faced by several challenges which many studies have tried to mitigate and proffer solutions to. This study proposed a methodology to mitigate the challenges of varying face poses and facial expressions which both face recognition-based classroom attendance systems have been reported to face over the years. This research leverages the use of 3D analysis of facial expressions to synthesize face image poses and facial expressions from a single input image of a subject. The study uses six basic facial expressions which it implements using a 3D model of set of points triangle mesh points connected in 3D space of 20 geometric feature points on human faces. The synthesized facial images are applied Local Binary Pattern Histogram (LBPH) texture operator on, to enable the extraction of histograms which are used to train a Support Vector Machine (SVM) classifier. The study uses two different human face databases; FEI and BU-3DFE, the two databases are used to evaluate the proposed methodology separately, with the FEI database images yielding an average of 71.78% performance accuracy and the BU- 3DFE database yielding an average of 78.57% performance accuracy. This designed methodology is further developed and applied as a recognition system for classroom attendance scenarios which led to the achievement of a satisfactory performance accuracy for individual subjects tested on it. Keywords: Classroom Attendance System, 3D Face Analysis, Face Recognition, Support Vector Machine, Local Binary Pattern Histogram
650		0	_aHuman face recognition (Computer science) _vDissertations, Academic
650		0	_aSupport vector machines _vDissertations, Academic
700	1		_aYurtkan, Kamil _esupervisor
942			_2ddc _cTS
999			_c288999 _d288999