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The Devil of Face Recognition Is in the Noise

  • Fei Wang
  • Liren Chen
  • Cheng Li
  • Shiyao Huang
  • Yanjie Chen
  • Chen Qian
  • Chen Change Loy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: (1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. (2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. (3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. (4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.SenseTime ResearchBeijingChina
  2. 2.University of California San DiegoSan DiegoUSA
  3. 3.Nanyang Technological UniversitySingaporeSingapore

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