  Recently, deep neural network has shown promising performance in face image
recognition. The inputs of most networks are face images, and there is hardly
any work reported in literature on network with face videos as input. To
sufficiently discover the useful information contained in face videos, we
present a novel network architecture called input aggregated network which is
able to learn fixed-length representations for variable-length face videos. To
accomplish this goal, an aggregation unit is designed to model a face video
with various frames as a point on a Riemannian manifold, and the mapping unit
aims at mapping the point into high-dimensional space where face videos
belonging to the same subject are close-by and others are distant. These two
units together with the frame representation unit build an end-to-end learning
system which can learn representations of face videos for the specific tasks.
Experiments on two public face video datasets demonstrate the effectiveness of
the proposed network.
