|dc.description.abstract||Multi-modal biometrics is one of the most promising avenues to address the performance
problems in biometrics-based personal authentication systems. While uni-modal biometric
systems have bolstered personal authentication better than traditional security methods, the
main challenges remain the restricted degrees of freedom, non-universality and spoof attacks
of the traits. In this research work, we investigate the performance improvement in bi-modal
biometrics authentication systems based on the physiological trait, the iris, and behavioral
trait, the signature.
We investigate a model to detect the largest non-occluded rectangular part of the iris as
a region of interest (ROI) from which iris features are extracted by a cumulative-sums-based
grey change analysis algorithm and Gabor Filters. In order to address the space complexity
of biometric systems, we proposed two majority vote-based algorithms which compute
prototype iris features codes as the reliable specimen templates. Experiments obtained a
success rate of 99.6%.
A text-based directional signature verification algorithm is investigated. The algorithm
verifies signatures, even when they are composed of symbols and special unconstrained cursive
characters which are superimposed and embellished. The experimental results show that
the proposed approach has an improved true positive rate of 94.95%.
A user-specific weighting technique, the user-score-based, which is based on the different
degrees of importance for the iris and signature traits of an individual, is proposed. Then,
an intelligent dual ν-support vector machine (2ν-SVM) based fusion algorithm is used to
integrate the weighted match scores of the iris and signature modalities at the matching
score level. The bi-modal biometrics system obtained a false rejection rate (FRR) of 0.008,
and a false acceptance rate (FAR) of 0.001.||en