GENDER PREDICTION FROM INLINE SIGNITURE BIOMETRICS / MUSTAPHA MOHAMED KARGBO; SUPERVISOR: ASST. PROF. DR. YASEMIN BAY AZYEREN

Yazar: Katkıda bulunan(lar):Dil: İngilizce 2022Tanım: 53 sheets; 31 cm. Includes CDİçerik türü:
  • text
Ortam türü:
  • unmediated
Taşıyıcı türü:
  • volume
Konu(lar): Tez notu: Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department Özet: ABSTRACT 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
Materyal türü: Thesis
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Materyal türü Geçerli Kütüphane Koleksiyon Yer Numarası Durum Notlar İade tarihi Barkod Materyal Ayırtmaları
Thesis Thesis CIU LIBRARY Tez Koleksiyonu Tez Koleksiyonu YL 2732 K27 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Management Information Systems Department T3061
Suppl. CD Suppl. CD CIU LIBRARY Görsel İşitsel YL 2732 K27 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Management Information Systems Department CDT3061
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Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department

Includes bibliography (sheets 48-53)

ABSTRACT
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

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