Many security applications require accurate identification of people, and research has shown that iris biometrics
can be a powerful identification tool. However, in order for iris biometrics to be used on larger populations,
error rates in the iris biometrics algorithms must be as low as possible. Furthermore, these algorithms need to
be tested in a number of different environments and configurations. In order to facilitate such testing, we have
collected more than 100,000 iris images for use in iris biometrics research. Using this data, we have developed a
number of techniques for improving recognition rates. These techniques include fragile bit masking, signal-level
fusion of iris images, and detecting local distortions in iris texture. Additionally, we have shown that large
degrees of dilation and long lapses of time between image acquisitions negatively impact performance.