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

2024-12-23

arXiv.org

AI Summary

This study addresses critical challenges in integrating facial expression analysis—encompassing both macro- and micro-expressions—into resource-constrained IoT applications such as intelligent healthcare and security surveillance, where real-time performance, robustness, and hardware limitations impose divergent requirements. Method: We propose, for the first time, a cross-modal lightweight modeling framework coupled with an edge–cloud collaborative paradigm for micro-expression recognition. Our approach synergistically integrates deep learning, temporal modeling, and heterogeneous IoT architecture to support multi-modal sensing (RGB, near-infrared, thermal imaging) and low-latency embedded inference. Contribution/Results: We establish the first IoT-oriented technical taxonomy for facial expression analysis, deliver a deployable system integration framework, and distill six fundamental cross-cutting challenges. The resulting paradigm enables scalable, low-latency, and adaptive real-time affect monitoring and covert threat detection in dynamic IoT environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Distinguish macro and micro facial expressions for emotion analysis
• Integrate facial expression analysis with IoT for real-time monitoring
• Explore applications in healthcare and security using IoT systems

Innovation

• IoT-enhanced MaE analysis for real-time patient monitoring
• IoT-based MiE detection for improved surveillance accuracy
• Integration of facial expression analysis with IoT systems