Dependency-aware Attention Control for
Unconstrained Face Recognition with Image Sets
Abstract. This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or
video) setting, we encounter a set of heterogeneous contents containing
orderless images and videos. The importance of each image is usually considered either equal or based on their independent quality assessment.
How to model the relationship of orderless images within a set remains
a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in the latent space. Specifically, we first present
a dependency-aware attention control (DAC) network, which resorts to
actor-critic reinforcement learning for sequential attention decision of
each image embedding to fully exploit the rich correlation cues among
the unordered images. Moreover, we introduce its sample-efficient variant
with off-policy experience replay to speed up the learning process. The
pose-guided representation scheme can further boost the performance at
the extremes of the pose variation