DIRA: disjoint-identity resolution adaptation for low-resolution face recognition

Jacky Chen Long Chai, C. Low, A. Teoh
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Abstract

Low-resolution face recognition (LRFR) intends to identify unknown poor-quality face images and is widely employed in real-world surveillance applications. While collecting a large-scale labeled low-resolution (LR) face dataset could be conducive, it is practically infeasible due to labor costs and privacy issues. In contrast, accessing high-resolution (HR) face datasets is relatively effortless. However, prevailing domain adaptation techniques are often tenuous as they demand sharing of similar face images at different resolutions. We propose disjoint-identity resolution adaptation (DIRA) to transfer substantial face semantic representations from HR to LR face images, despite disjoint identities and limited labeled LR images. We accredit that continuous adversarial learning between HR-LR resolution alignment and segregation renders effective feature extraction and discriminative LR face representation. Our experimental results show a notable performance boost over the recent state-of-the-art methods for the challenging realistic low-resolution face recognition task.
低分辨率人脸识别的分离身份分辨率适应
低分辨率人脸识别(LRFR)旨在识别未知的低质量人脸图像,并广泛应用于现实世界的监控应用。虽然收集大规模标记低分辨率(LR)人脸数据集可能是有益的,但由于劳动力成本和隐私问题,实际上是不可行的。相比之下,访问高分辨率(HR)人脸数据集相对轻松。然而,当前的领域自适应技术往往是脆弱的,因为它们需要在不同分辨率下共享相似的人脸图像。我们提出了disjoint-identity resolution adaptation (DIRA),将大量的人脸语义表征从HR图像转移到LR图像,尽管不脱节的身份和有限的标记LR图像。我们认为,HR-LR分辨率对齐和分离之间的持续对抗性学习可以有效地提取特征和判别LR人脸表示。我们的实验结果表明,在具有挑战性的低分辨率人脸识别任务中,该方法的性能显著提高。
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