Score Fusion by Maximizing the Area under the ROC Curve

Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impostor priors or the costs for the different errors. Empirical results on several datasets show the effectiveness of the proposed approach.

[1]  Samy Bengio,et al.  The Expected Performance Curve , 2003, ICML 2003.

[2]  C. Marroccoa,et al.  Maximizing the area under the ROC curve by pairwise feature combination , 2008 .

[3]  Roberto Paredes,et al.  Simultaneous learning of a discriminative projection and prototypes for Nearest-Neighbor classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Bojan Cukic,et al.  A Classification Approach to Multi-biometric Score Fusion , 2005, AVBPA.

[5]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[6]  Patrick Verlinde,et al.  Multi-modal identity verification using support vector machines (SVM) , 2000, Proceedings of the Third International Conference on Information Fusion.

[7]  Samy Bengio,et al.  A Score-Level Fusion Benchmark Database for Biometric Authentication , 2005, AVBPA.

[8]  John P. Baker,et al.  Fusing multimodal biometrics with quality estimates via a Bayesian belief network , 2008, Pattern Recognit..

[9]  Claudio Marrocco,et al.  Exploiting AUC for optimal linear combinations of dichotomizers , 2006, Pattern Recognit. Lett..

[10]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jaihie Kim,et al.  Biometric scores fusion based on total error rate minimization , 2008, Pattern Recognit..

[12]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[13]  Michael C. Mozer,et al.  Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic , 2003, ICML.

[14]  Enrique Vidal,et al.  Learning prototypes and distances (LPD). A prototype reduction technique based on nearest neighbor error minimization , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..