Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

Emad Barsoum, Cha Zhang, Cristian Canton Ferrer, Zhengyou Zhang
8/3/2016
cs.CV

Official code: microsoft/FERPlus (662 stars, last commit 2023-06-12).

Abstract

Crowd sourcing has become a widely adopted scheme to collect ground truth labels. However, it is a well-known problem that these labels can be very noisy. In this paper, we demonstrate how to learn a deep convolutional neural network (DCNN) from noisy labels, using facial expression recognition as an example. More specifically, we have 10 taggers to label each input image, and compare four different approaches to utilizing the multiple labels: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. We show that the traditional majority voting scheme does not perform as well as the last two approaches that fully leverage the label distribution. An enhanced FER+ data set with multiple labels for each face image will also be shared with the research community.

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Code Implementations(1)

microsoft/FERPlusOfficial100%

This is the FER+ new label annotations for the Emotion FER dataset.

662169PythonSep 14, 20163 years agoNOASSERTION

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Cite this paper

@article{barsoum2016training,
  title  = {Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution},
  author = {Emad Barsoum and Cha Zhang and Cristian Canton Ferrer and Zhengyou Zhang},
  year   = {2016},
  eprint = {1608.01041},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1608.01041}
}

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