Score level fusion of hand based biometrics using t-norms

A multimodal biometric system amalgamates the information from multiple biometric sources to alleviate the limitations in performance of each individual biometric system. In this paper a multimodal biometric system employing hand based biometrics (i.e. palmprint, hand veins, and hand geometry) is developed. A general combination approach is proposed for the score level fusion which combines the matching scores from these hand based modalities using t-norms due to Hamacher, Yager, Weber, Schweizer and Sklar. This study aims at exploring the potential usefulness of t-norms for multimodal biometrics. These norms deal with the real challenge of uncertainty and imperfection pervading the different sources of knowledge (scores from different modalities). We construct the membership functions of fuzzy sets formed from the genuine and imposter scores of each of the modalities considered. The fused genuine score and imposter scores are obtained by integrating the fuzzified genuine scores and imposter scores respectively from each of the modalities. These norms are relatively very simple to apply unlike the other methods (example SVM, decision trees, discriminant analysis) as no training or any learning is required here. The proposed approach renders very good performance as it is quite computationally fast and outperforms the score level fusion using the conventional rules (min, max, sum, median) The experimental evaluation on a database of 100 users confirms the effectiveness of score level fusion. The preliminary results are encouraging in terms of decision accuracy and computing efficiency.