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Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

  • Xiaohang Zhan
  • Ziwei Liu
  • Junjie Yan
  • Dahua Lin
  • Chen Change Loy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

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.

Notes

Acknowledgement

This work is partially supported by the Big Data Collaboration Research grant from SenseTime Group (CUHK Agreement No. TS1610626), the General Research Fund (GRF) of Hong Kong (No. 14236516, 14241716).

Supplementary material

474192_1_En_35_MOESM1_ESM.pdf (21.3 mb)
Supplementary material 1 (pdf 21847 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.CUHK - SenseTime Joint Lab, The Chinese University of Hong KongShatinHong Kong
  2. 2.SenseTime Group LimitedBeijingChina
  3. 3.Nanyang Technological UniversitySingaporeSingapore

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