An efficient fusion strategy for multimodal biometric system

This paper proposes an efficient multi-step fusion strategy for multimodal biometric system. Fusion is done at two stages i.e., algorithm level and modality level. At algorithm level the important steps involved are normalization, data elimination and assignment of static and dynamic weights. Further, the individual recognizers are combined using sum of scores technique. Finally the integrated scores from individual traits are passed to decision module. Fusion at decision level is done using Support Vector Machines (SVM). The SVM is trained by the set of matching scores and it classifies the data into two known classes i.e., genuine and imposters. The system is tested on database collected for 200 individuals and is showing a considerable increase in accuracy (overall accuracy 98.42%) compared to individual traits.