DocumentCode :
1203298
Title :
Multimodal Biometric System Using Rank-Level Fusion Approach
Author :
Monwar, Md Maruf ; Gavrilova, Marina L.
Author_Institution :
Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB
Volume :
39
Issue :
4
fYear :
2009
Firstpage :
867
Lastpage :
878
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. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. 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, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. 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. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.
Keywords :
authorisation; biometrics (access control); face recognition; image fusion; image matching; principal component analysis; regression analysis; visual databases; Fisher linear discriminant method; biometric database; biometric matcher; face recognition; identity authentication; logistic regression approach; multimodal biometric system; multiple domain expert; principal component analysis; rank-level fusion integration method; Biometric identification system; logistic regression; multibiometric system; pattern recognition; principal component analysis (PCA); rank-level fusion; Algorithms; Artificial Intelligence; Biometry; Dermatoglyphics; Ear; Face; Humans; Image Processing, Computer-Assisted; Logistic Models; Pattern Recognition, Automated; Principal Component Analysis; ROC Curve; Software; Voice;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2008.2009071
Filename :
4804708
Link To Document :
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