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Yayın Biometric recognition using bio-signals(Işık Üniversitesi, 2017-04-14) Dursun, Ceren; Gürkan, Hakan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıThe main objective of the project is to increase the recognition rate by establishing a multimodal biometric recognition system that uses two di_erent biometric characteristics, as bio-signals. Today, institutions use biometric recognition systems quite often to provide security for many areas such as information security and physical security. The importance of these systems increases day by day in the direction of technological development and increasing demand. Recognition systems based on biometric characteristics are more reliable, because of the possibility of forgetting or losing knowledge in the recognition systems based on knowledge (eg: password) and the possibility of being stolen or guessed by third persons in the biometric recognition based on possessed (eg: card). However, the fraud techniques are also developing in the direction of technological developments and biometric characteristics cannot be renewed in case of imitation, hence the use of multiple biometrics recognition system may be a solution to this problem. At the same time, the use of multiple biometrics increases in the security of systems. In this thesis, a biometric recognition system, which uses the lectrocardiogram (ECG) and speech signals of the person, was created. Since there was not enough time and possibility, an arti_cial database was generated with obtaining these signals from various sources. First, the MIT-BIH Arrhythmia Database was used for ECG signals. This database consists of 48 ECG signals, which belong to 22 females and 26 males. In ccordance with this database, a database was created for the speech signals, which were obtained from the website, given in [1]. The features of the biometric signals were extracted by AC / DCT (Autocorrelation/ Discrete Cosine Transform) method for ECG signals and by Mel Frequency Cepstrum Coe_cients (MFCCs) method for speech signals. The data, which were obtained from the feature extraction, were then classi_ed by the Gaussian Mixture Model (GMM) method. The scores, which were obtained from the classi_cation process, were fused as a single individual's data, and the decision-making step was passed. Recognition rates were obtained in the decision making step. The recognition rate for the ECG signal was %87.50 and 42 persons were matched correctly. The recognition rate for 2 seconds speech signals was %58.33 and 28 persons were matched correctly. Normalization was applied before the fusion of these two datasets. The recognition rate after the fusion was %70.83 and 34 persons were matched correctly. However, when the recognition rates are considered, it has been observed that the recognition rate, which obtained after the fusion, is lower than recognition rate of the ECG signals. Therefore, instead of 2 seconds speech signals, 10 seconds speech signals were used. In this case, the recognition rate of the speech signals was %97.9 and 47 persons were matched correctly. Then, normalization was applied again and two datasets were fused. After the fusion, the rate of recognition reached %95.8 and 46 persons were matched correctly.Yayın Fingertip ECG signal based biometric recognition system(Işık Üniversitesi, 2016-05-10) Güven, Gökhan; Gürkan, Hakan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıThe idea is the; realize biometric recognition system by using ECG signal which was began to use in the last 10 years. Until now, ECG based biometric systems have been developed by using the database which ECG signals are taken from the subjects’ chest by using patient monitor or high speed data acquisition systems. For constructing database, most researchers have used three disposable ECG electrodes on subjects’ chest to extract the ECG signal. Because of that, ECG based biometric systems were being considered hard to use. For this reason, we want to make a system that is easy to carry, and easy to apply on subjects. In most of biometric systems, ECG database have constructed by using ECG signal of the subjects that were taken by using electrodes which were located in left and right side of the heart with a reference electrode on right leg. However, in our system, the database consisted of the ECG signals which were taken by using patient’s right and left thumb. The difference of our system with the others is; there is EMG noise which is in the same frequency range with ECG signal. Because of frequencies are the same, it is very hard to eliminate with filters. For this reason, database was enhanced by applying the different filters on ECG signal to reduce the noises for higher recognition rate. First week of the dataset -which was obtained by using ECG signals of 30 people in two separate weeks- is extracted the personality information by performing AC/DCT and MFCC methods and is reserved as training dataset. ECG signals which obtained by AC/DCT methods in second week are called as test dataset. First candidate is determined by putting the AC/DCT features of an unknown person into the LDA classifier. In the meantime, same person’s MFCC features put into the LDA classifier and the second candidate is determined. If these two candidates are the same, they are labeled as A and B person. If they are not the same person, then QRS frames of the proximate two candidates obtained from AC/DCT features and QRS frames of the proximate two candidates obtained from MFCC features are sent to K-NN algorithm. QRS frames of these 4 candidates are sorted ascending according to the proximity to the QRS frame of unknown person, and nearest candidate to unknown QRS segment is labeled as A and B person. Proposed method was reached to success at rate of %96 average frame recognition.Yayın Fingertip electrocardiogram and speech signal based biometric recognition system(Işık Üniversitesi, 2021-12-27) Güven, Gökhan; Güz, Ümit; Gürkan, Hakan; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Doktora ProgramıFingertip electrocardiogram and speech signal based biometric recognition system In this research work, we presented a one-dimensional CNN-based person identification system which depends on the combination of both speech and ECG modalities to improve the overall performance compared to traditional systems. The proposed method has two approach: one is to develop combination of textindependent speech and fingertip ECG fusion system, the other one is to develop a robust rejection algorithm to prevent unauthorized access to the fusion system. In addition to the system robustness, we have developed an ECG spike and inconsistent beats removing algorithm, which detect and remove the problems caused by either portable fingertip ECG devices or movements of the patients. First approach has been tested on 30, 45, 60, 75 and 90 people which were taken from LibriSpeech Corpus database and combination of both CYBHi and our private fingertip ECG database. The 3-fold cross validation test setup has been conducted while system working time was set to 10 seconds. In the first experiment, we achieved 90.22% accuracy rate for 90 people for ECG based system. For the speech based system, 97.94% accuracy rate has achieved for 90 people. For the combination of both system, 99.92% accuracy rate has been achieved. For the second approach, 90 people for ECG and Speech database were being used as genuine class, 26 people as imposter class, and after the performance evaluation in optimum rejection thresholds, 71.08% accuracy rate for imposters rejection and 71.05% accuracy rate for genuine recognition has achieved for ECG based system. For the speech based system, imposter class were 87.82% accurately rejected while genuine classes were 86.48% accurately identified. The combination of both system has achieved 91.68% accuracy for genuine identification rate whereas 96.05% accuracy for imposter rejection.Yayın Tuş vuruşlarına dayalı kimlik doğrulama yöntemleri: evrimi, zorlukları ve gelecek yönelimlerinin kapsamlı bir incelemesi(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-09-23) Gündoğan, Nebil Vural; Çeliktaş, Barış; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Siber Güvenlik Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in CybersecurityTuş vuruşu (keystroke) ile kimlik doğrulama, bireylerin klavye kullanımındaki yazım ritimlerini ve zamanlama desenlerini analiz ederek kimlik doğruluğunu sağlayan sofistike bir davranışsal biyometrik yöntemdir. Bu yöntemin dikkat çekici avantajları arasında, kullanıcıdan ek bir işlem gerektirmemesi, herhangi bir ek donanım ihtiyacı doğurmaması ve maliyet etkinliği bulunmaktadır. Gelişmiş bilişim altyapılarında ve güvenlik hassasiyeti yüksek uygulamalarda, kullanıcıyı tanımak için sürekli izleme ve ikinci faktör doğrulama gerekliliği artarken, tuş vuruşu temelli yöntemler bu gereksinimlere düşük maliyetli ve sezgisel bir çözüm sunmaktadır. Bu çalışma, tuş vuruşu dinamik kimlik doğrulama yöntemleri ile ilgili literatürü sistematik olarak incelemektedir. İlk olarak, farklı setler ve özellikleri gözden geçirilmekte, ardından makine öğrenimi (ML), derin öğrenme (DL) ve hibrit modeller performans, güvenlik ve kullanılabilirlik açısından karşılaştırılmaktadır. Ayrıca, mevcut metodolojiler OWASP Kimlik Doğrulama Hile Sayfası aracılığıyla sunulan kılavuz bağlamında ele alınarak, güvenlik açıkları ve olası saldırılar analiz edilmektedir. Hibrit modellerin, daha yüksek doğruluk ve üstün dayanıklılık açısından otonom ML veya DL yöntemlerinden daha iyi performans gösterdiği ortaya çıkmaktadır. Gelecekteki yönelimler açısından, federatif öğrenme (FL), açıklanabilir yapay zekâ (XAI) ve multimodal biyometrik füzyon, gizlilik, açıklana bilirlik ve platformlar arasında genelleştirile bilirlik açısından daha sağlam çözümler üretme konusunda umut vaat etmektedir. Değerlendirme kapsamında, söz konusu modellerin masaüstü sistemlerde, web tabanlı platformlarda ve mobil cihazlarda sergilediği performanslar karşılaştırmalı olarak analiz edilmiştir. Elde edilen veriler, bazı modellerin yüksek doğruluk oranlarına ulaştığını ancak kullanıcı deneyiminde sürtünme (friction) oluşturduğunu; diğer modellerin ise kullanıcı dostu yapısına karşın daha düşük güvenlik sunduğunu ortaya koymaktadır. Bu bağlamda, sistem seçiminde güvenlik, doğruluk ve kullanıcı konforu arasında bir denge kurulması gerektiği sonucuna varılmıştır. Bu bağlamda önerdiğimiz hibrit doğrulama çerçevesi, derin sinir ağlarının sınıflandırma yeteneklerini anomali tespit teknikleriyle birleştirmekte ve bağlamsal farkındalığa sahip özellik çıkarımı ile uyarlanabilir eşikleme mekanizmaları kullanmaktadır. Böylelikle, modelimiz hem yeni kullanıcı davranışlarına uyum sağlayabilmekte hem de sahtecilik girişimlerine karşı yüksek hassasiyetle yanıt verebilmektedir. Ayrıca, önerilen çerçevenin farklı kullanım bağlamlarında—örneğin sürekli oturum denetimi veya ikinci faktör doğrulama senaryolarında—uygulanabilirliği değerlendirildiğinde, sistemin ölçeklenebilirliği ve uygulama kolaylığı da ön plana çıkmaktadır. Sonuç olarak, elde edilen bulgular, tuş vuruşu doğrulama sistemlerinin, özellikle diğer biyometrik yöntemlerle bütünleştiğinde veya bağlamsal verilerle desteklendiğinde, yüksek güvenlik gerektiren uygulamalarda etkin, uyarlanabilir ve kullanıcı dostu bir çözüm sunduğunu göstermektedir. Çalışma sadece literatürde bulunan yöntemlerin kapsamlı bir karşılaştırmasını yapmakla kalmayıp, aynı zamanda gelecekteki çalışmalarda metodolojik seçimler için bir kılavuz da çizmektedir.
