mlsanityData to 7 Oct 2026

Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey

arXiv 2412.17616 ↗First posted 23 Dec 2024cs.AIcited by 5

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Abstract (arXiv)

Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios. IoT-enhanced MaE analysis enables real-time monitoring of patient emotions, facilitating improved mental health care in smart healthcare. Similarly, IoT-based MiE detection enhances surveillance accuracy and threat detection in smart security. Our work aims to provide a comprehensive overview of research progress in facial expression analysis and explores its potential integration with IoT systems. We discuss the distinctions between our work and existing surveys, elaborate on advancements in MaE and MiE analysis techniques across various learning paradigms, and examine their potential applications in IoT. We highlight challenges and future directions for the convergence of facial expression-based technologies and IoT systems, aiming to foster innovation in this domain. By presenting recent developments and practical applications, our work offers a systematic understanding of the ways of facial expression analysis to enhance IoT systems in healthcare, security, and beyond.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 65 of its 288 references that are in the map. First posted on arXiv 23 Dec 2024.

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65 of 288 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Towards Stable Cross-Domain Depression Recognition under Missing Modalitiescited by 3
  2. 2025Exploring and Mitigating Fawning Hallucinations in Large Language Modelscited by 1
  3. 2025A Nesterov-Accelerated Byzantine-Robust Federated Learningcited by 1
  4. 2025CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networkscited by 0
  5. 2026AffectSeek: Agentic Affective Understanding in Long Videos under Vague User Queriescited by 0