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 .
@cite_10 proposed a multimodal biometric score level fusion scheme using Gaussian Mixture Model and Monte Carlo Method. The authors fused face, speech and palmprint modalities and showed that their method gives higher performance than other fusion schemes, such as Simple sum rule, Weighted sum rule, Fishers Linear Discriminate Analysis (FLD), and Likelihood Ratio (LR) methods.
@cite_10
2099352997
Multimodal biometric fusion is gaining more attraction among researchers in recent days. As multimodal biometric consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a novel frame work for optimal combination of match scores using Gaussian Mixture Model(GMM) and Monte Carlo method. The proposed fusion approach has the ability to handle 1) Small size of match scores as is more commonly encountered in biometric fusion and 2) Arbitrary distribution of match scores. The proposed fusion scheme is compared with more robust fusion schemes such as SUM rule, weighted SUM rule, Fishers Linear Discriminate Analysis (FLD) and Likelihood Ratio(LR) method. Extensive experiments are carried out on three different build multimodal biometric databases. Experimental results indicate that proposed fusion scheme achieves higher performance as compared with other fusion techniques.