Jayanta Basak, Kiran Kate, Vivek Tyagi & Nalini Ratha International Journal of Biometrics and Bioinformatics (IJBB), Volume (6) : Issue (5) : 2012 123 QPLC: A Novel Multimodal Biometric Score Fusion Method Jayanta Basak basakjayanta@yahoo.com NetApp Advanced Technology Group Bangalore, India Kiran Kate kiran.kate@gmail.com IBM Research, Singapore Vivek Tyagi vivetyag@in.ibm.com IBM Research, New Delhi, India Nalini Ratha ratha@us.ibm.com IBM T J Watson Research Center Hawthorne, USA Abstract 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. 1. INTRODUCTION Biometrics-based authentication systems have been shown to be extremely useful in many security applications because of the non-repudiation functionality. However, these systems suffer from many shortcomings: the errors associated with the biometrics such as the false accept rate and false reject rate can impact the performance of the system; the failure to acquire and failure to enroll error rates can also impact the coverage of the population; fake biometrics e.g., latex fingers, face masks etc. can be used to fool biometrics systems. In order to overcome these problems, multi-biometrics systems have been proposed which is also known as biometric fusion. The fusion can be at various levels: signal (data), features, and classifiers. Several examples of biometric fusion methods have been reported in the literature. Fusion could involve more than one biometrics modality such as finger and face; involve more than one classifier e.g., face with two different matchers; involve more than one sample of a biometrics e.g., two samples of the same finger; involve more than one sensing modality in a particular mode e.g., face acquisition using infra red imaging and regular color cameras. Each method of fusion described above would have some advantage over a unimodal system. The biometrics fusion problem is very interesting problem from a research and practical use perspective. The general area of fusion in the computer vision community has been studied extensively while its application to biometrics has been a relatively recent phenomenon. Early