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

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