The receiver operating characteristics (ROC) curve has been extensively used for performance evaluation in multimodal biometrics fusion. However, the processes of fusion classifier design and the final ROC performance evaluation are usually conducted separately. This has been inevitable because the ROC, when taken from the error counting point of view, does not have a well-posed structure linking to the fusion classifier of interest. In this work, we propose to optimize the ROC performance directly according to the fusion classifier design. The area under the ROC curve (AUC) will be used as the optimization objective since it provides a good representation of the ROC performance. Due to the piecewise cumulative structure of the AUC, a smooth approximate formulation is proposed. This enables a direct optimization of the AUC with respect to the classifier parameters. When a fusion classifier has linear parameters, computation of the solution to optimize a quadratic AUC approximation is surprisingly simple and yet effective. Our empirical experiments on biometrics fusion show strong evidences regarding the potential of the proposed method.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence