A Survey on Facial Expression Recognition of Static and Dynamic Emotions
Yan Wang, Shaoqi Yan, Yang Liu, Wei Song, Jing Liu, Yang Chang, Xinji, Mai, Xiping Hu, Wenqiang Zhang, Zhongxue Gan

TL;DR
This comprehensive survey reviews recent advances in facial expression recognition, covering static and dynamic methods, challenges, datasets, and future research directions in the evolving field of AI-driven emotion analysis.
Contribution
It provides an in-depth analysis of both static and dynamic FER methods, categorizes approaches based on challenges, and proposes future research directions, filling gaps in existing reviews.
Findings
Benchmark performances of recent FER methods
Identification of key challenges in static and dynamic FER
Analysis of datasets and evaluation criteria
Abstract
Facial expression recognition (FER) aims to analyze emotional states from static images and dynamic sequences, which is pivotal in enhancing anthropomorphic communication among humans, robots, and digital avatars by leveraging AI technologies. As the FER field evolves from controlled laboratory environments to more complex in-the-wild scenarios, advanced methods have been rapidly developed and new challenges and apporaches are encounted, which are not well addressed in existing reviews of FER. This paper offers a comprehensive survey of both image-based static FER (SFER) and video-based dynamic FER (DFER) methods, analyzing from model-oriented development to challenge-focused categorization. We begin with a critical comparison of recent reviews, an introduction to common datasets and evaluation criteria, and an in-depth workflow on FER to establish a robust research foundation. We then…
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Taxonomy
TopicsFace and Expression Recognition · Emotion and Mood Recognition
