Class-Specific Kernel Selection for Verification Problems

The single-class verification framework is gaining increasing attention for problems involving authentication and retrieval. In this paper, nonlinear features are extracted using the kernel trick. The class of interest is modeled by using all the available samples rather than a single representative sample. Kernel selection is used to enhance the class specific feature set. A tunable objective function is used to select the kernel which enables the adjustment of the false acceptance and false rejection rates. The errors caused due to the presence of highly similar classes are reduced by using a two-stage hierarchical authentication framework. The performance of the resulting verification system is demonstrated on the hand-geometry based authentication problem with encouraging results.