Empirical Techniques to Detect and Mitigate the Effects of Irrevocably Evolving User Profiles in Touch-Based Authentication Systems

Touch dynamics (or touch based authentication) refers to a behavioral biometric for touchscreen devices wherein a user is authenticated based on his/her executed touch gestures. In this work, we present the results of a series of empirical techniques to detect habituation in the user's touch profile, its detrimental effect on authentication accuracy and strategies to overcome these effects. Habituation here refers to changes in the user's profile and/or noise within it due to the user's familiarization with the device and software application. The results of this work show that habituation causes the user's touch profile to evolve significantly and irrevocably over time even after the user is familiar with the device and software application. This phenomenon considerably degrades classifier accuracy. We show that this effect can be best mitigated using approximately 300 most recent user inputs and retraining the classifier. The retrained classifier can be used with minimal increase in error rate on up to 75 new user inputs. This results in an error rate of 3.68% that sets the benchmark in this field for a realistic test setup. Finally, we quantify the benefits of vote-based reclassification of predicted class labels and show that this technique is vital for achieving high accuracy in realistic touch-based authentication systems.