Iris Indexing and Ear Classification

To identify an individual using a biometric system, the input biometric data has to be typically compared against that of each and every identity in the existing database during the matching stage. The response time of the system increases with the increase in number of individuals (i.e., database size), which is not acceptable in real time monitoring or when working on large scale data. This thesis addresses the problem of reducing the number of database candidates to be considered during matching in the context of iris and ear recognition. In the case of iris, an indexing mechanism based on Burrows Wheeler Transform (BWT) is proposed. Experiments on the CASIA version 3 iris database show a significant reduction in both search time and search space, suggesting the potential of this scheme for indexing iris databases. The ear classification scheme proposed in the thesis is based on parameterizing the shape of the ear and assigning it to one of four classes: round, rectangle, oval and triangle. Experiments on the MAGNA database suggest the potential of this scheme for classifying ear databases