# A Survey on Facial Expression Recognition of Static and Dynamic Emotions

arXiv: https://arxiv.org/abs/2408.15777 · HTML: https://mlsanity.com/paper/2408.15777/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2024.

## Built on by (most cited first, 15 of 15)

- [BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change](https://mlsanity.com/paper/2505.19328/) (2025)
- [Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions](https://mlsanity.com/paper/2604.11730/) (2026)
- [Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models](https://mlsanity.com/paper/2509.19595/) (2025)
- [Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2512.00641/) (2025)
- [Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features](https://mlsanity.com/paper/2510.11223/) (2025)
- [EmoScene: A Dual-space Dataset for Controllable Affective Image Generation](https://mlsanity.com/paper/2604.00933/) (2026)
- [An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images](https://mlsanity.com/paper/2504.21309/) (2025)
- [From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition](https://mlsanity.com/paper/2507.11892/) (2025)
- [FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision](https://mlsanity.com/paper/2511.21245/) (2025)
- [MotivNet: Evolving Meta-Sapiens into an Emotionally Intelligent Foundation Model](https://mlsanity.com/paper/2512.24231/) (2025)
- [Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling](https://mlsanity.com/paper/2604.12777/) (2026)
- [Evaluating multimodal emotion recognition in proactive conversational agents: A user study](https://mlsanity.com/paper/2605.20200/) (2026)
- [Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions](https://mlsanity.com/paper/2606.08612/) (2026)
- [LG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence Distillation](https://mlsanity.com/paper/2608.23880/) (2026)
- [Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.06497/) (2026)

## Abstract (arXiv)

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 systematically review representative approaches addressing eight main challenges in SFER (such as expression disturbance, uncertainties, compound emotions, and cross-domain inconsistency) as well as seven main challenges in DFER (such as key frame sampling, expression intensity variations, and cross-modal alignment). Additionally, we analyze recent advancements, benchmark performances, major applications, and ethical considerations. Finally, we propose five promising future directions and development trends to guide ongoing research. The project page for this paper can be found at https://github.com/wangyanckxx/SurveyFER.