Given a large collection of unlabeled face images, we address the problem of clustering faces into an unknown number of identities. This problem is of interest in social media, law enforcement, and other applications, where the number of faces can be of the order of hundreds of million, while the number of identities (clusters) can range from a few thousand to millions. To address the challenges of run-time complexity and cluster quality, we present an approximate Rank-Order clustering algorithm that performs better than popular clustering algorithms (k-Means and Spectral). Our experiments include clustering up to 123 million face images into over 10 million clusters. Clustering results are analyzed in terms of external (known face labels) and internal (unknown face labels) quality measures, and run-time. Our algorithm achieves an F-measure of 0.87 on the LFW benchmark (13 K faces of 5,749 individuals), which drops to 0.27 on the largest dataset considered (13 K faces in LFW + 123M distractor images). Additionally, we show that frames in the YouTube benchmark can be clustered with an F-measure of 0.71. An internal per-cluster quality measure is developed to rank individual clusters for manual exploration of high quality clusters that are compact and isolated.

中文翻译：

---

通过身份将数百万张面孔聚类

给定大量未标记的面部图像，我们解决了将面部聚类为未知数量的身份的问题。这个问题在社交媒体，执法和其他应用程序中引起关注，在这些应用程序中，面孔的数量可以达到数亿个，而身份（集群）的数量可以从几千个到数百万个不等。为了解决运行时复杂性和集群质量的挑战，我们提出了一种近似的Rank-Order聚类算法，其性能优于流行的聚类算法（k-Means和Spectral）。我们的实验包括将多达1.23亿张面部图像聚类到超过1000万个聚类中。根据外部（已知的面部标签）和内部（未知的面部标签）质量度量以及运行时间来分析聚类结果。我们的算法实现了F-measure为0。在LFW基准上为87（5,749个人的13K张面孔），在所考虑的最大数据集（LFW + 123M干扰图像中的13K面孔）上，该数字降至0.27。此外，我们显示YouTube基准中的帧可以采用0.71的F度量进行聚类。开发了内部每个集群的质量度量，以对单个集群进行排名，以手动探索紧凑且孤立的高质量集群。