  We present a Deep Convolutional Neural Network (DCNN) architecture for the
task of continuous authentication on mobile devices. To deal with the limited
resources of these devices, we reduce the complexity of the networks by
learning intermediate features such as gender and hair color instead of
identities. We present a multi-task, part-based DCNN architecture for attribute
detection that performs better than the state-of-the-art methods in terms of
accuracy. As a byproduct of the proposed architecture, we are able to explore
the embedding space of the attributes extracted from different facial parts,
such as mouth and eyes, to discover new attributes. Furthermore, through
extensive experimentation, we show that the attribute features extracted by our
method outperform the previously presented attribute-based method and a
baseline LBP method for the task of active authentication. Lastly, we
demonstrate the effectiveness of the proposed architecture in terms of speed
and power consumption by deploying it on an actual mobile device.
