Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and their identities are exclusive from the labeled ones. Our main insight is that although the class information is not available, we can still faithfully approximate these semantic relationships by constructing a relational graph in a bottom-up manner. We propose Consensus-Driven Propagation (CDP) to tackle this challenging problem with two modules, the "committee" and the "mediator", which select positive face pairs robustly by carefully aggregating multi-view information. Extensive experiments validate the effectiveness of both modules to discard outliers and mine hard positives. With CDP, we achieve a compelling accuracy of 78.18% on MegaFace identification challenge by using only 9% of the labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all labels are employed.

本篇论文的研究表明，对于人脸识别问题，采用无标注数据也可以有效替代有标注数据，通过构建从底部往上的关系图，使用Consensus-Driven Propagation (CDP)方法挑选出正面对的脸部图像，使用CDP方法只使用了9%的标注数据，在MegaFace识别挑战上获得了78.18%的准确率，进一步验证了该方法的有效性。

大规模未标记数据中的一致性传播人脸识别