With the increasing emphasis on the automatic personal identification applications, biometrics especially fingerprint identification is the most reliable and widely accepted technique. So, the idea is to investigate the effects of varying image quality on a multialgorithm approach based on minutiae-based and pore-based matchers. These two matchers provide complementary information commonly exploited by score-level fusion. The idea of quality-based score fusion has been incorporated into this multiple algorithm approach. This paper formulates an evidence theoretic multimodal fusion approach using belief functions that takes into account the variability in image characteristics. The effectiveness of our approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered
