Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
This work addresses label noise in crowd-sourced data for facial expression recognition, which is an incremental improvement in a domain-specific context.
The paper tackled the problem of noisy labels in crowd-sourced facial expression recognition by comparing four approaches to train deep convolutional neural networks, showing that methods leveraging label distribution outperform majority voting.
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.