Abstract
M.Phil. (Electrical And Electronic Engineering)
The enormous increase in technology advancement and the need to secure information
e ectively has led to the development and implementation of iris image acquisition technologies
for automated iris recognition systems. The iris biometric is gaining popularity
and is becoming a reliable and a robust modality for future biometric security. Its wide
application can be extended to biometric security areas such as national ID cards, banking
systems such as ATM, e-commerce, biometric passports but not applicable in forensic
investigations. Iris recognition has gained valuable attention in biometric research due
to the uniqueness of its textures and its high recognition rates when employed on high
biometric security areas. Identity veri cation for individuals becomes a challenging task
when it has to be automated with a high accuracy and robustness against spoo ng attacks
and repudiation. Current recognition systems are highly a ected by noise as a
result of segmentation failure, and this noise factors increase the biometric error rates
such as; the FAR and the FRR. This dissertation reports an investigation of score level
fusion methods which can be used to enhance iris matching performance. The fusion
methods implemented in this project includes, simple sum rule, weighted sum rule fusion,
minimum score and an adaptive weighted sum rule. The proposed approach uses
an adaptive fusion which maps feature quality scores with the matcher. The fused scores
were generated from four various iris matchers namely; the NHD matcher, the WED
matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching
scores before fusion, raw scores were normalized using the tanh-estimators method,
because it is e cient and robust against outliers. The results were tested against two
publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric
system measurements namely the AUC and the EER. The results of these two
measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA
right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate
(EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and
with the EER = 0:038 for UBIRIS images.