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Η μέθοδος αναγνώρισης μέσω του τρόπου πληκτρολόγησης αποτελεί μία μέθοδο αναγνώρισης βιομετρικών χαρακτηριστικών με στόχο να ελαχιστοποιηθεί ο κίνδυνος κλοπής των προσωπικών κωδικών των πελατών ενός συστήματος. Το παρόν βιομετρικό σύστημα βασίζεται στο σενάριο ότι ο ρυθμός με τον οποίο ένα πρόσωπο πληκτρολογεί είναι ξεχωριστός. Το βιομετρικό σύστημα έχει δύο λειτουργίες, την εγγραφή των πελατών στο σύστημα και τη σύγκριση. Για την εγγραφή απαραίτητη είναι η εξαγωγή των προτύπων των πελατών τα οποία αποθηκεύονται στη βάση δεδομένων του συστήματος ενώ για στη σύγκριση το πρότυπο του χρήστη συγκρίνεται με το πρότυπο του πελάτη που ισχυρίζεται ότι είναι. Στη παρούσα εργασία η εξαγωγή τον προτύπων πραγματοποιείται μέσω μία σειράς αλγοριθμικών διαδικασιών. Αρχικά η μονοδιάστατη χαρακτηριστική χρονοσειρά του χρήστη μετατρέπεται μέσω της μεθόδου Method of Delays σε ένα πολυδιάστατο διάνυσμα που λειτουργεί ως χαρακτηριστικό της ακολουθίας. Στη συνέχεια χρησιμοποιούμε δύο διαφορετικές μεθόδους για να υπολογίσουμε τις ανομοιότητες μεταξύ των πολυδιάστατων διανυσμάτων που προέκυψαν. Οι δύο αυτές μέθοδοι είναι οι Wald-Wolfowitz test και Mutual Nearest Point Distance. Οι τιμές αυτές τοποθετούνται σε έναν πίνακα κάθε στοιχείο του οποίου αναπαριστά την ανομοιότητα μεταξύ δύο χρονοσειρών. Ο πίνακας αυτός μπορεί είτε να αποτελέσει το σύνολο των προτύπων των χρηστών είτε να χρησιμοποιηθεί ως είσοδο στη μέθοδο Multidimensional Scaling που χρησιμοποιείται για μετατροπή του πίνακα ανομοιοτήτων σε διανύσματα και εξαγωγή νέων προτύπων. Τέλος, προτείνουμε ως επέκταση της εργασίας την εκπαίδευση του βιομετρικού συστήματος με χρήση των τεχνικών Support Vector Machines. Για τη λειτουργία της σύγκρισης εξάγουμε πάλι το πρότυπο του χρήστη με την ίδια διαδικασία και το συγκρίνουμε με μία τιμή κατωφλίου. Τέλος, ο έλεγχος της αξιοπιστίας του συστήματος πραγματοποιείται μέσω της χρήσης τριών δεικτών απόδοσης, Equal Error Rate, False Rejection Rate και False Acceptance Rate. / The identification method via keystroke is a method of identifying biometric features in order to minimize the risk of theft of personal codes of customers of a system. The present biometric system based on the scenario that the rate at which a person presses the keyboard buttons is special. The biometric system has two functions, the enrollment of customers in the system and their test. For enrollment, it is necessary to export standards of customers’ information stored in the system database and for the test the standard of the user is compared with the standard of the user that is intended to be the customer. In the present thesis the export of the standards is taken place via a series of algorithmic procedures. Initially,the one dimensional characteristic time series of user is converted, by the technique Method of Delays, in a multidimensional vector that acts as a feature of the sequence. Then, two different methods are used to compute the dissimilarities between multidimensional vectors obtained. These two methods are the Wald-Wolfowitz test and the Mutual Nearest Point Distance. These values are placed in an array, each element of which represents the dissimilarity between two time series. This table can be either the standards of users or can be entry in the Multidimensional Scaling method used to convert the table disparities in vectors and then produce new standards of users. Finally, we propose as extension of our thesis, the training of biometric system with using the techniques of Support Vector Machine. For the test, again the pattern of the user is extracted with the same procedure and is compared to a threshold. Finally, the reliability of the system is carried out through the use of three performance indicators, Equal Error Rate, False Rejection Rate and False Acceptance Rate.

Voice recognition has become a more focused and researched field in the last century,and new techniques to identify speech has been introduced. A part of voice recognition isspeaker verification which is divided into Front-end and Back-end. The first componentis the front-end or feature extraction where techniques such as Mel-Frequency CepstrumCoefficients (MFCC) is used to extract the speaker specific features of a speech signal,MFCC is mostly used because it is based on the known variations of the humans ear’scritical frequency bandwidth. The second component is the back-end and handles thespeaker modeling. The back-end is based on the Gaussian Mixture Model (GMM) andGaussian Mixture Model-Universal Background Model (GMM-UBM) methods forenrollment and verification of the specific speaker. In addition, normalization techniquessuch as Cepstral Means Subtraction (CMS) and feature warping is also used forrobustness against noise and distortion. In this paper, we are going to build a speakerverification system and experiment with a variance in the amount of training data for thetrue speaker model, and to evaluate the system performance. And further investigate thearea of security in a speaker verification system then two methods are compared (GMMand GMM-UBM) to experiment on which is more secure depending on the amount oftraining data available.This research will therefore give a contribution to how much data is really necessary fora secure system where the False Positive is as close to zero as possible, how will theamount of training data affect the False Negative (FN), and how does this differ betweenGMM and GMM-UBM.The result shows that an increase in speaker specific training data will increase theperformance of the system. However, too much training data has been proven to beunnecessary because the performance of the system will eventually reach its highest point and in this case it was around 48 min of data, and the results also show that the GMMUBM model containing 48- to 60 minutes outperformed the GMM models.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Computer based assessments have become one of the largest growing sectors in both nonacademic and academic establishments. Successful computer based assessments require security against impersonation and fraud and many researchers have proposed the use of Biometric technologies to overcome this issue. Biometric technologies are defined as a computerised method of authenticating an individual (character) based on behavioural and physiological characteristic features. Basic biometric based computer based assessment systems are prone to security threats in the form of fraud and impersonations. In a bid to combat these security problems, keystroke dynamic technique and facial biometric recognition was introduced into the computer based assessment biometric system so as to enhance the authentication ability of the computer based assessment system. The keystroke dynamic technique was measured using latency and pressure while the facial biometrics was measured using principal component analysis (PCA). Experimental performance was carried out quantitatively using MATLAB for simulation and Excel application package for data analysis. System performance was measured using the following evaluation schemes: False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Accuracy (AC), for a comparison between the biometric computer based assessment system with and without the keystroke and face recognition alongside other biometric computer based assessment techniques proposed in the literature. Successful implementation of the proposed technique would improve computer based assessment’s reliability, efficiency and effectiveness and if deployed into the society would improve authentication and security whilst reducing fraud and impersonation in our society.