Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
This is an incremental work that organizes and reviews existing research for researchers in computer vision and affective computing.
This paper provides a comprehensive survey of automatic facial expression recognition methods, covering RGB, 3D, thermal, and multimodal approaches, and introduces a new taxonomy to classify state-of-the-art techniques, datasets, and benchmarks.
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.