Open-set face recognition describes a scenario where unknown subjects, unseen
during the training stage, appear on test time. Not only it requires methods
that accurately identify individuals of interest, but also demands approaches
that effectively deal with unfamiliar faces. This work details a scalable
open-set face identification approach to galleries composed of hundreds and
thousands of subjects. It is composed of clustering and an ensemble of binary
learning algorithms that estimates when query face samples belong to the face
gallery and then retrieves their correct identity. The approach selects the
most suitable gallery subjects and uses the ensemble to improve prediction
performance. We carry out experiments on well-known LFW and YTF benchmarks.
Results show that competitive performance can be achieved even when targeting
scalability.

本研究介绍了一种可扩展的开放式人脸识别方法，用于包含数百和数千个主题的图库。通过聚类和二元学习算法的集合，该方法可以准确识别感兴趣的个体，并有效处理陌生人脸。它选择最合适的图库主题，并使用集合方法来提高预测性能。通过在众所周知的 LFW 和 YTF 基准测试上进行实验，结果表明即使针对可扩展性，也可以实现竞争性能。