Increased Use of Available Image Data Decreases Errors in Iris Biometrics

Iris biometrics is used in a number of different applications, such as frequent flyer programs, identification of prisoners, and border control in the United Arab Emirates. However, governments interested in using iris biometrics have still found difficulties using it on large populations. Further improvements in iris recognition are required in order to enable this technology to be used in more settings. In this dissertation, we describe three methods of reducing error rates for iris biometrics. We define and employ a metric called the fragile bit distance which uses the locations of less stable bits in an iris template to improve performance. We also investigate signal fusion of multiple frames in an iris video to achieve better recognition performance than is possible using single still images. Third, we present a study of what features are useful for identification in the periocular region. Periocular biometrics is still an emerging field of research, but we anticipate that fusing periocular information with iris information will result in a more robust biometric system. A final contribution of this work is a study of how iris biometrics performs on twins. Our experiments confirm prior claims that iris biometrics is capable of differentiating between twins. However, we additionally show that there is texture information in the iris that is not encoded by traditional iris biometrics systems. Our experiments suggest that human examination of pairs of iris images for forensic purposes may be feasible. Our results also suggest that development of different approaches to automated iris image analysis may be useful.