Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks.
In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.

Keywords: multibiometrics, biometric fusion, fingerprint, finger-vein, authentication systems, identity management, privacy, security

The majority of deployed biometric systems today use information from a single biometric technology for verification or identification. Large-scale biometric systems have to address additional demands such as larger population coverage and demographic diversity, varied deployment environment, and more demanding performance requirements. Today’s single modality biometric systems are finding it difficult to meet these demands, and a solution is to integrate additional sources of information to strengthen the decision process. A multibiometric system combines information from multiple biometric traits, algorithms, sensors, and other components to make a recognition decision. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. The last five years have seen an exponential growth in research and commercialisation activities in this area, and this trend is likely to continue. Therefore, here we propose a novel multimodal biometric authentication approach using iris and fingerprint traits at score-level. We principally explore the fusion of iris and fingerprint biometrics and their potential application as biometric identifiers. The individual comparison scores obtained from the iris and fingerprints are combined at score-level using three-score normalization techniques (Min-Max, Z-Score, Hyperbolic Tangent) and four score fusion approaches (Minimum Score, Maximum Score Simple Sum and User Weighting). The fused-score is utilised to classify an unknown user into the genuine or impostor.

Keywords: Biometrics; Authentication; Fingerprint Recognition; Iris Recognition; Identity Management; Image Quality;Score Normalization; Score-level Fusion.