A Comparative Performance Analysis of Multimodal-Multialgorithm System Framework Based on Rank Level Fusion

Abstract

The Unimodal biometric framework have various fundamental issues, for example, intra-class alteration, noisy data, failure-to-enroll, spoofing attacks, unacceptable error rate and non-universality. To defeat this shortcoming multibiometric is a decent alternative where we can utilize at least two individual modalities. This paper gives a comparative analysis of multi-algorithm and multimodal system framework based on rank level fusion. An effective combination strategy that integrates information given by different domain specialist dependent on rank level fusion approach is utilized to enhance the presentation of the framework. The rank of individual matcher is combined using the highest rank, Borda count, weighted Borda count, nonlinear weighted approach and Bucklin combination approach. The outcomes of the results show there is a noteworthy exhibition enhancement in the identification accuracy can be accomplished when contrasted those from unimodal frameworks. The outcomes also reveal that combination of individual modalities can enhance the biometric system performance. The experiment based on multimodal (NIST BSSR1 multimodal database of fingerprint and face) and multialgorithm (Hong Kong Polytechnic University database of palmprint) system shows an improvement in term of the Rank-1 identification rate of the system.