Evaluation of Biometric Authentication Systems through Visualisation of Partitioned and Bundled Power-Graphs

Biometric authentication systems verify the identity of individuals based on what they are. As they are error prone, they can reject genuine individuals or accept impostors. Researchers of the field quantify the quality of their algorithm by benchmarking it on several databases. However, although the standard evaluation metrics state the performance of their system, they are unable to explain the reasons of their errors. This paper presents a novel way to visualize the evaluation results of a biometric authentication system which helps to find which individuals or samples are sources of errors. This knowledge could help to fix the algorithms. A biometric database of scores is modeled as a partitioned power-graph with nodes representing biometric samples and power-nodes representing individuals. A novel recursive edge bundling method is also applied to reduce clutter. This proposal has been successfully applied on several biometric databases and has proved its efficiency.