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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets

verfasst von : Xiaofeng Liu, B. V. K. Vijaya Kumar, Chao Yang, Qingming Tang, Jane You

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Nächstes Kapitel Volumetric Performance Capture from Minimal Camera Viewpoints
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Metadaten
Titel
Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets
verfasst von
Xiaofeng Liu
B. V. K. Vijaya Kumar
Chao Yang
Qingming Tang
Jane You
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01252-6_34

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