Facial expression recognition (FER) survey: a vision, architectural elements, and future directions
Sana Ullah1, Jie Ou1, Yuanlun Xie1
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Peerj. Computer Science
|June 10, 2024
Summary
Facial expression recognition (FER) systems are advancing rapidly, impacting various sectors. This study explores FER technologies, applications, challenges, and future directions to drive innovation in emotion measurement.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Facial Expression Recognition (FER) is a rapidly evolving field within computer vision.
- FER systems have diverse applications across education, marketing, health, and transportation.
- Accurate emotion measurement remains a significant challenge in current FER research.
Purpose of the Study:
- To provide a comprehensive overview of Facial Expression Recognition (FER) technologies.
- To discuss the architectural elements, applications, and leading companies in the FER domain.
- To explore the integration of FER with the Internet of Things (IoT) and Cloud computing, and identify future research directions.
Main Methods:
- Systematic review utilizing the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) method.
- In-depth analysis of current FER technologies, including basic and compound emotion recognition.
- Examination of challenges and future research avenues in FER.
Main Results:
- The study details the foundational principles and architectural components of FER systems.
- It highlights the wide-ranging applications and use-cases of FER technology.
- The research identifies key challenges and proposes future directions for FER advancement.
Conclusions:
- Overcoming identified challenges in FER is crucial for future breakthroughs.
- Integrating FER with IoT and Cloud computing offers new possibilities.
- This research aims to guide future studies in revolutionizing facial expression recognition.
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