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A Survey on Facial Expression Recognition of Static and Dynamic Emotions

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arxiv 2408.15777 v1 pith:MVBJCKWR submitted 2024-08-28 cs.CV

Yan Wang , Shaoqi Yan , Yang Liu , Wei Song , Jing Liu , Yang Chang , Xinji Mai , Xiping Hu
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Wenqiang Zhang Zhongxue Gan
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classification cs.CV
keywords expressionchallengesdynamicstaticanalyzedevelopmentdferemotions
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

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  2. ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    ARGen generates high-fidelity dynamic facial expression videos using affective semantic injection and adaptive reinforcement diffusion to improve emotion recognition models facing data scarcity and long-tail distributions.

  3. ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception

    cs.CV 2026-04 conditional novelty 6.0 of 10

    ARGen uses AU-guided prompts and a reinforcement-learned diffusion strategy to synthesize scarce-class facial expression videos that improve dynamic emotion recognition.

  4. FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Adding direct FLAME expression supervision from 3D scans plus an emotion-recognition head improves AffectNet facial-expression prediction from reconstructed faces without hurting geometric accuracy.

  5. Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    DuSE is a new dual-stream model for dynamic facial expression recognition that explicitly models cognitive priming and conceptual knowledge integration to reach state-of-the-art accuracy on in-the-wild benchmarks.

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    Standard deep multimodal models and LLM zero-shot inference achieve only limited performance on video ambivalence/hesitancy recognition for digital health personalization.

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    Multimodal deep learning for ambivalence/hesitancy recognition in videos yields limited results on the BAH dataset, highlighting the need for improved spatio-temporal and cross-modal fusion methods.

  8. Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

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  9. Evaluating multimodal emotion recognition in proactive conversational agents: A user study

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