Publication

Abstract : In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher’s linear Discriminant methods for individual matchers (face,iris, and fingerprint) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers.The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data.