Context Awareness for Improved Continuous Face Authentication on Mobile Devices

Abstract

In this paper we produce a transparent continuous facial authentication scheme that is aware of the current activity and illumination context. It has recently been shown that contextual differences in environment and user activity have significant effects on the accuracy of the scheme. Furthermore, cross-comparisons or permutations of facial data captured in different contexts can see detrimental authentication results. Our scheme models both ambient light and activity accelerometer data for contextual awareness. This data is used to train separate facial classifiers for different activity and illumination contexts (e.g.: when a user is walking). When a face is captured for classification, a window of accelerometer and ambient light data is firstly classified to select which classifier has been trained on facial data obtained from the current context. In our experiments we use two state-of-the-art facial datasets. We describe the architecture and performance of our context recognition components. We show that activity and illumination awareness decreases the equal error rate by 4.05% and 4.39%, respectively. We also perform experiments to show the required accuracy needed from contextual components to yield facial recognition improvements.