Deep Facial Expression Recognition: A Survey

TL;DR

This survey reviews deep learning methods for facial expression recognition, discussing datasets, algorithms, challenges like overfitting and variations, and future research directions to improve robustness in real-world conditions.

Contribution

It provides a comprehensive overview of deep FER, including system pipelines, datasets, algorithms, and challenges, highlighting recent advances and future opportunities.

Findings

Deep neural networks improve FER accuracy in challenging conditions.

Benchmark performances show progress but highlight remaining challenges.

Survey identifies key issues and future directions for robust deep FER.

Abstract

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.