Authentication Feature and Model Selection using Penalty Algorithms

Continuous Authentication (CA) is the process of verifying the identity of the user of an electronic device repeatedly while the device is in use. Existing research in the field employs metrics such as Equal Error Rate (EER) and/or the Receiver Operating Characteristic (ROC) to evaluate the performance in the same way as ‘entry-point’ biometric authentication schemes. These metrics have various shortcomings with regard to CA as they fail to model the practical implications of the authentication process. We would like to discuss and get feedback on performance evaluation techniques that capture practical aspects of the authentication system including the length and frequency of times an impostor reaches different authentication levels and similarly for the genuine user. Our preliminary results show that a multi-level authentication system is not only more accurate than a binary diagnosis but it allows for high level of accuracy. We posit that further research is needed in developing such a metric for truly evaluating a CA system.