Face-Based Active Authentication On Mobile Devices

As mobile devices are becoming more ubiquitous, it becomes important to continuously verify the identity of the user during all interactions rather than just at login time. This paper investigates the effectiveness of methods for fully-automatic face recognition in solving the Active Authentication (AA) problem for smartphones. We report the results of face authentication using videos recorded by the front camera. The videos were acquired while the users were performing a number of tasks under three different ambient conditions to capture the type of variations caused by the 'mobility' of the devices. An inspection of these videos reveal a combination of favorable and challenging properties unique to smartphone face videos. In addition to variations caused by the mobility of the device, other challenges in the dataset include occlusion, occasional pose changes, blur and face/fiducial points localization errors. We evaluate still image and image set-based authentication algorithms using intensity features extracted around fiducial points. The recognition rates drop dramatically when enrollment and test videos come from different sessions. We will make the dataset and the computed features publicly available to help the design of algorithms that are more robust to variations due to factors mentioned above.