论文标题

使用辅助未标记的数据来增强无约束的面部识别

Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data

论文作者

Shi, Yichun, Jain, Anil K.

论文摘要

近年来,面部识别取得了重大进展,这可能部分归因于大型标签面部数据集的可用性。但是,由于这些数据集中的面通常包含有限的程度和类型的变化,因此所产生的训练模型概括为更现实的不受约束的面部数据集。尽管收集具有较大差异的标签面孔可能会有所帮助,但由于隐私和劳动力成本,实际上是不可行的。相比之下,从不同领域获取大量未标记的面孔会更容易,这些面孔可用于规范面部表征的学习。我们提出了一种使用此类未标记面的方法来学习可推广的面部表征,我们既不假设对身份标签的访问也不是未标记的图像。对无约束数据集的实验结果表明,少数具有足够多样性的未标记数据可以(i)在识别性能方面带来可观的增益,并且(ii)与少于一半的标记数据相结合时优于监督基线。与最先进的面部识别方法相比,我们的方法进一步提高了它们在具有挑战性的基准(例如IJB-B,IJB-C和IJB-S)上的性能。

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since the faces in these datasets usually contain limited degree and types of variation, the resulting trained models generalize poorly to more realistic unconstrained face datasets. While collecting labeled faces with larger variations could be helpful, it is practically infeasible due to privacy and labor cost. In comparison, it is easier to acquire a large number of unlabeled faces from different domains, which could be used to regularize the learning of face representations. We present an approach to use such unlabeled faces to learn generalizable face representations, where we assume neither the access to identity labels nor domain labels for unlabeled images. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can (i) lead to an appreciable gain in recognition performance and (ii) outperform the supervised baseline when combined with less than half of the labeled data. Compared with the state-of-the-art face recognition methods, our method further improves their performance on challenging benchmarks, such as IJB-B, IJB-C and IJB-S.

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