Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains iii, 101 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 96-101).

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Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 92 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 89-92).

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Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 95 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 92-95).

M.Sc. / A Smart Card looks like a normal plastic card that we use every day, but its capabilities and advantages are huge. Inside the card there is a small microprocessor capable of doing operations on data. With memory available on the card, data can be stored in a safe and secure location. This card can be used for various applications and is a big improvement on all of its predecessors. These applications can be anything from SIM cards in a cell phone to credit cards and cards used for access control. The Smart Card offers us better security and offline identification because of its own embedded microprocessor. The combination of Smart Cards with biometrics for security reasons will be a logical step and the ideal way to identify the person as the true owner of the card. This dissertation will investigate the use of contact Smart Cards in the University environment, more specifically as a University student card. The Smart Card will be combined with a fingerprint to enforce better security. The main purpose is to use the Smart Card and the biometric property for access control at various places on campus.

M.Ing. (Electrical And Electronic Engineering) / Iris recognition systems have attracted much attention for their uniqueness, stability and reliability. This recognition system is composed of four main modules, namely, iris acquisition, iris segmentation, feature extraction and encoding and - nally iris matching. However, performance of this system is a ected by poor image quality. In this research, a novel iris image quality assessment method based on character component is presented. This method is composed of two steps, individual assessment of character quality parameters and fusion of estimated quality parameters using Principal Component Analysis (PCA). The de ned quality parameters considered in this research are entropy, sharpness, occlusion, dilation, area ratio, contrast and blur. The designed technique was tested on three databases: Chinese Academy of Science Institute of Automation (CASIA), University of Beira Interior (UBIRIS) and Internal Collection (IC). Individual assessment of quality parameters has shown that dilation, sharpness and blur have more in uence on the quality score than the other parameters. The images were classi ed into two categories (good and bad) by human visual inspection. The e ect of the individual parameters on each database is illustrated, with CASIA exhibiting higher quality scores than the UBIRIS and IC databases. Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) were used to evaluate the performance of the proposed quality assessment algorithm. A k-fold cross validation technique was employed to the classi ers to obtain unbiased results. Two performance measures were used to rate the proposed algorithm, namely, Correct Rate (CR) and Area Under the Curve (AUC). Both performance measures showed that SVM classi er outperforms LDA in correctly classifying the quality of the images in all three databases. The experimental results demonstrated that the proposed algorithm is capable of detecting poor quality images as it yields an e ciency of over 84% and 90% in CR and AUC respectively. The use of character component to assess quality has been found to be su cient, though there is a need to develop a better technique for standardization of quality. The results found using a SVM classi er a rms the proposed algorithm is well-suited for quality assessment.

M.Sc. / Determining the identity of a person has become vital in today’s world. Emphasis on security has become increasingly more common in the last few decades, not only in Information Technology, but across all industries. One of the main principles of security is that a system only be accessed by a legitimate user. According to the ISO 7498/2 document [1] (an international standard which defines an information security system architecture) there are 5 pillars of information security. These are Identification/Authentication, Confidentiality, Authorization, Integrity and Non Repudiation. The very first line of security in a system is identifying and authenticating a user. This ensures that the user is who he/she claims to be, and allows only authorized individuals to access your system. Technologies have been developed that can automatically recognize a person by his unique physical features. This technology, referred to as ‘biometrics’, allows us to quickly, securely and conveniently identify an individual. Biometrics solutions have already been deployed worldwide, and it is rapidly becoming an acceptable method of identification in the eye of the public. As useful and advanced as unimodal (single biometric sample) biometric technologies are, they have their limits. Some of them aren’t completely accurate; others aren’t as secure and can be easily bypassed. Recently it has been reported to the congress of the U.S.A [2] that about 2 percent of the population in their country do not have a clear enough fingerprint for biometric use, and therefore cannot use their fingerprints for enrollment or verification. This same report recommends using a biometric system with dual (multimodal) biometric inputs, especially for large scale systems, such as airports. In this dissertation we will investigate and compare multimodal biometric techniques, in order to determine how much of an advantage lies in using this technology, over its unimodal equivalent.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science, School of Computer Science and Applied Mathematics. May 2017. / Biometric features have been widely implemented to be utilized for forensic and civil applications. Amongst many different kinds of biometric characteristics, the fingerprint is globally accepted and remains the mostly used biometric characteristic by commercial and industrial societies due to its easy acquisition, uniqueness, stability and reliability. There are currently various effective solutions available, however the fingerprint identification is still not considered a fully solved problem mainly due to accuracy and computational time requirements. Although many of the fingerprint recognition systems based on minutiae provide good accuracy, the systems with very large databases require fast and real time comparison of fingerprints, they often either fail to meet the high performance speed requirements or compromise the accuracy. For fingerprint matching that involves databases containing millions of fingerprints, real time identification can only be obtained through the implementation of optimal algorithms that may utilize the given hardware as robustly and efficiently as possible. There are currently no known distributed database and computing framework available that deal with real time solution for fingerprint recognition problem involving databases containing as many as sixty million fingerprints, the size which is close to the size of the South African population. This research proposal intends to serve two main purposes: 1) exploit and scale the best known minutiae matching algorithm for a minimum of sixty million fingerprints; and 2) design a framework for distributed database to deal with large fingerprint databases based on the results obtained in the former item. / GR2018