· 2016
Training deep networks for facial expression recognition with crowd-sourced label distribution
- 877citations
- 2016year
Short summary
A novel method trains deep networks on noisy, crowd-sourced facial expression labels by leveraging label distribution, outperforming traditional majority voting.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a method to train DCNNs on noisy, crowd-sourced labels for facial expression recognition.
- Compared majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss for label utilization.
- Probabilistic label drawing and cross-entropy loss, which leverage label distribution, performed better than majority voting.
- Introduced and will release an enhanced FER+ dataset with multiple labels per image.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science