Bi-modal biometrics authentication on iris and signature.
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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.