000			02760nam a22002777a 4500
003			KOHA
005			20230419101907.0
008			230227d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2665 _bD27 2022
100	1		_aDastres, Roza
245	1	0	_aMASKED FACE RECOGNITION / _cROZA DASTRES; SUPERVISOR: ASSOC. PROF. DR. KAMİL YURTKAN
264			_c2022
300			_a63 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Technology Department
504			_aIncludes bibliography (sheets 56-63)
520			_aABSTRACT Face biometrics have been recognized as a feature for identification for many years, and most researchers and scholars recognize these biometrics as one of the most reliable and accurate biometrics. But in recent years, these biometrics have been used to identify other human characteristics such as age, race, gender, and so on. Regarding the vast range of applications for facial recognition in identification, significant research has been done in this field, although it falls short of ideals. With the advent of COVID19 at the end of the twentieth century, the challenge of masking face recognition was created. According to extensive research on face research in this field, existing face-based identification methods have received less attention in various mask challenges. The primary goal of this research is to find answers to the following questions of masked face recognition systems. Therefore, it is necessary to invent a method that has a good response to stable changes and time, in order to recognize the masked faces. This study uses a "Real World Masked Face Dataset" image database. The method presented in this research, after preprocessing, Morphological Operations has been utilized to derive features from pictures of people's faces by using the KNN descriptors. To classify the properties extracted by the descriptors, two families of super plane and neighborhood classifiers k are the nearest neighbors. Finally, after implementing the ideas considered in the employed method and comparing the results of the classification, 72.5% for the desired database for classification with k nearest neighbor have been obtained for classification. Keywords: Masked Face Recognition, Face Biometric
650		0	_aHuman face recognition (Computer science) _vDissertations, Academic
700	1		_aYurtkan, Kamil _esupervisor
942			_2ddc _cTS
999			_c289798 _d289798