000			03231nam a22002897a 4500
003			KOHA
005			20230414092205.0
008			230227d2022 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2732 _bK27 2022
100	1		_aKargbo, Mustapha Mohamed
245	1	0	_aGENDER PREDICTION FROM INLINE SIGNITURE BIOMETRICS / _cMUSTAPHA MOHAMED KARGBO; SUPERVISOR: ASST. PROF. DR. YASEMIN BAY AZYEREN
264			_c2022
300			_a53 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 Management Information Systems Department
504			_aIncludes bibliography (sheets 48-53)
520			_aABSTRACT With the improvement and huge introduction of technological devices and systems, the use of biometrics has become a very important inclusion in our lives to increase the level of confidence and security with access privileges to approved or certified individuals. One of the most popular applications of biometrics is signature biometrics, especially in terms of identification and/or verification process; however, it is not limited to this, and it reveals high-level discriminative physiological features. Although modalities of soft biometrics do provide details about an individual which is important for forensics when specified identities cannot be provided, they also include non-unique attributes of individuals like gender, age, ethnicity, presence of scars or tattoos, etc. The purpose of this study is to predict gender from the online signatures of an individual applying different feature sets during the classification process. In this study, Cyprus International University's handwriting and signature database is used for the investigation and prediction of gender from online signature biometrics. The database comprises 134 participants’ signatures, providing 8040 signature samples in total. The prediction is executed in three fundamental steps; feature selection, feature extraction, and classification. The feature selection method using WEKA helped to achieve the goal of gaining a better understanding of pattern recognition in gender prediction from online signature biometrics and this helped to achieve high accuracy levels using different classification methods such as KNN, Random Forest, and JRip. There were 5 experiments conducted in total using different sample sizes and feature sets and high gender prediction accuracy rates were obtained in all experiments which proves that signature biometrics contain valuable information especially gender and it is possible to predict gender from one’s signature. The highest result was obtained in Experiment 3 with 97 % accuracy. Keywords: Biometric, Gender Prediction, Online Signature, Machine Learning
650		0	_aBiometry _vDissertations, Academic
650		0	_aMachine learning _vDissertations, Academic
700	1		_aAzyeren, Yasemin Bay _esupervisor
942			_2ddc _cTS
999			_c289794 _d289794