Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
It addresses the challenge of heterogeneous image sets for face verification and identification, offering a novel method for improved accuracy in unconstrained scenarios.
The paper tackles face recognition from image sets by modeling relationships among unordered images using a dependency-aware attention control network with reinforcement learning, achieving state-of-the-art results on benchmarks like IJB-A and IJB-S with accuracies up to 98.7%.
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.