In this paper, first, a new fusion technique, referred to as hybrid fusion (HBF) technique, based on feature-level fusion and the best unimodal system for multimodal biometric system recognition, is proposed. Secondly, a new weighting technique, referred to as mean-extrema based confidence weighting (MEBCW) technique, based on the scores obtained from feature-level fusion and the best unimodal system, is proposed. Finally, a weighted hybrid fusion, referred to as weighted hybrid fusion (WHBF) technique, is developed by incorporating MEBCW in HBF, in order to improve the overall recognition rate of a multimodal biometric system. The performance of the proposed method, in terms of equal error rate and genuine acceptance rates @5.3% and @7.2% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric systems using the proposed fusions is superior to that of the uni-biometric systems or to that of the system using existing level of fusions.

The performance of a multi-biometric system can be improved using an efficient normalization technique under the simple sum-rule-based score-level fusion. It can also be further improved using normalization techniques along with a weighting method under the weighted sum-rule-based score-level fusion. In this paper, at first, we present two anchored score normalization techniques based on the genuine and impostor scores. Specifically, the proposed normalization techniques utilize the information of the overlap region between the genuine and impostor scores and their neighbors. Second, we propose a weighting technique that is based on the confidence of the matching scores by considering the mean-to-maximum of genuine scores and mean-to-minimum of impostor scores. A multi-biometric system having three biometric traits, fingerprint, palmprint, and earprint, is utilized to evaluate the performance of the proposed techniques. The performance of the multi-biometric system is evaluated in terms of the equal error rate and genuine acceptance rate @0.5% false acceptance rate. The receiver operating characteristics are also plotted in terms of the genuine acceptance rate as a function of the false acceptance rate.

Dissimilarities in equal error rates (EERs) of multiple matchers heavily influence the performance of multi-biometric systems. A normalization technique aims at improving the recognition rate of such a system. In view of this, in this paper, an anchored normalization technique, referred to as improved anchored min-max (IAMM) technique for a multimodal biometric system, is developed. In the proposed technique, the anchor value is computed from the raw matching score sets corresponding to each of the modalities used in the system. This anchor value does not require a priori knowledge of the equal error rates and genuine/impostor score distributions of the individual matchers used in the system. It takes into account the average and variations of the score values that occur more than once in each score set. The performance of IAMM, in terms of EER and genuine acceptance rates @10% and @20% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric system using the proposed normalization technique is superior to that of the uni-biometric systems or to that of the system using the existing normalization techniques.

The performance of multiple matchers heavily influence the recognition accuracy of a multi-biometric system under the simple sum-rule-based score-level fusion. In this paper, a weighting technique, referred to as score reliability based weighting (SRBW) technique, is developed to estimate weights for the matchers in order to improve the recognition rate of multi-biometric systems at the score level. In the proposed technique, the reliabilities are computed directly from the raw matching scores obtained from the individual matchers. The proposed weighting technique does not require a priori knowledge of the rankings of matching scores, or the equal error rates, or the genuine/impostor score distributions of the individual matchers used in the system. The experimental results show that the performance of a multi-biometric system using the proposed weighting technique is superior to that of the uni-biometric systems or to that of the multi-biometric systems using the existing weighting techniques in terms of equal error rate and genuine acceptance rate at 1% false acceptance rate.