GETSEL: Gallery entropy for template selection on large datasets

The ability of a biometric system to reliably recognize registered individuals significantly depends on the kind and amount of variation that the exploited biometric trait may undergo throughout acquisitions. Those variations may be due both to acquisition devices, or to different environment settings, or to modification of the trait appearance. One of the strategies to address changes in biometric features is to store more templates for the same person, in order to increase the chances to identify her. The problem arises to choose the templates to store in a way which actually achieves better performance, while avoiding flooding the system with an excessively huge gallery. This work proposes an approach for the selection of the best templates for face recognition, which is based on a notion of gallery entropy, and can be also used for large datasets. Though relying on a clustering process, its main achievement is to automatically derive the best number of clusters/prototypes per subject without requiring to fixing it in advance. Comparative tests with existing approaches show that it is a very promising solution.