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QPLC: A Novel Multimodal Biometric Score Fusion Method
Jayanta Basak, Kiran Kate, Vivek Tyagi, Nalini Ratha
Pages - 123 - 134     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Multimodal Biometrics, Normalization, Quantile Transformation, SVM
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
CITED BY (8)  
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Dr. Jayanta Basak
NetApp - India
Miss Kiran Kate
IBM Research - Singapore
Mr. Vivek Tyagi
IBM Research - India
Miss Nalini Ratha
IBM T J Watson Research Center Hawthorne - United States of America