Benchmarking Deep Facial Expression Recognition: An Extensive Protocol
  with Balanced Dataset in the Wild
Gianmarco Ipinze Tutuianu Yang Liu Ari Alamäki Janne Kauttonen
Facial expression recognition (FER) is a crucial part of human-computer
interaction. Existing FER methods achieve high accuracy and generalization
based on different open-source deep models and training approaches. However,
the performance of these methods is not always good when encountering practical
settings, which are seldom explored. In this paper, we collected a new
in-the-wild facial expression dataset for cross-domain validation. Twenty-three
commonly used network architectures were implemented and evaluated following a
uniform protocol. Moreover, various setups, in terms of input resolutions,
class balance management, and pre-trained strategies, were verified to show the
corresponding performance contribution. Based on extensive experiments on three
large-scale FER datasets and our practical cross-validation, we ranked network
architectures and summarized a set of recommendations on deploying deep FER
methods in real scenarios. In addition, potential ethical rules, privacy
issues, and regulations were discussed in practical FER applications such as
marketing, education, and entertainment business.