Design Of Multiple Enrollment Based Fingerprint Recognition Systems

ABSTRACT

Using multiple enrollment can improve recognition performance in fingerprint recognition systems; but there are several technical and operational challenges to implementing multiple enrollment based fingerprint recognition systems. Multiple enrollment based fingerprint recognition systems still have low recognition accuracies, poor matching speeds, and consume a lot of memory making it difficult to implement them in real world scenarios. Also, most of multiple enrollment based fingerprint recognition systems have been designed mainly based on minutiae approaches but not others such as correlation and pattern based approaches hence limiting implementation. The purpose of this research was to provide a novel multiple enrollment fingerprint recognition approach that further improves recognition accuracy, the matching speed and reduce memory consumption in multiple enrollment based fingerprint recognition systems as well as allow for implementation using non-minutiae methods. In this thesis, a literature survey of the state of the art in multiple enrollment for fingerprint recognition was first performed. A list of laboratories working on multiple enrollment for fingerprint recognition was also generated. This literature survey serves as a quick overview of the state of the art in multiple enrollment for fingerprint recognition for the past two decades. This thesis evaluates the effectiveness of using multiple enrollment in fingerprint recognition systems. A Spectral minutiae based multiple enrollment algorithm was designed and used together with existing fingerprint recognition techniques to carry out the evaluation. The experimentation results and evaluations show that multiple enrollment as whole outperforms single enrollment. Multiple enrollment in experiment one improved the recognition performance by 83.33% from EER of 0.75% to EER of 0.13% with FVC2000-DB2 fingerprint database, and by 75.55% from EER of 1.14% to EER of 0.28% with the SAS-DB2 fingerprint database. On the other hand, the multiple enrollment in experiment two improved the recognition performance by 71.51% from EER of 6.14% to EER of 1.75% with the FVC2000-DB2 fingerprint database and improved recognition performance by 53.61% from EER of 14.97% to EER of 6.94% with SAS-DB2 fingerprint database. A comparison with single enrollment and other multiple enrollment results in literature shows that our algorithms were superior by over 38.1% in terms of recognition performance.