Earticle

초록

영어

Emotion recognition is rapidly advancing within the realm of artificial intelligence (AI), spurred by progress in deep learning, multimodal data handling, and broader availability of large datasets. This paper offers an in-depth overview of emotion recognition strategies, focusing on bio-metric signals (e.g., audio, visual, physiological, and brain signals), conventional machine learning methods, and the latest deep learning architectures, including Transformers, two-dimensional Con-volution Neural Networks (CNNs) 2D CNNs, and three-dimensional CNNs (3D CNNs). We further examine multimodal systems and the role of Large Language Models (LLMs) in merging textual, audio, and video information for more precise emotion assessments. Key challenges such as real-time implementation, data biases, and cultural diversity are also highlighted. Ethical issues related to privacy and the potential misuse of emotion recognition technologies receive attention, as does a discussion of emerging applications in healthcare, human-computer interaction (HCI), mental health monitoring, and education. In sum, this review aims to contribute to the scholarly conversa-tion around evolving emotion recognition methodologies and their applications in practical systems.

목차

Abstract
1. Introduction
2. Emotion Recognition Techniques
2.1. Audio-Based Emotion Recognition
2.2. Visual-Based Emotion Recognition
2.3. Physiological and Brain Signal-Based Emotion Recognition
2.4. Text-Based Emotion Recognition
3. Multimodal Approaches Using Large Language Models (LLMs) and Video Data
3.1. Text and Video-Based Emotion Recognition
3.2. Combining Audio, Visual, and Textual Inputs
4. Key Challenges in Emotion Recognition
4.1. Dataset Biases
4.2. Cross-Cultural Variations
4.3. Real-Time Performance
4.4. Privacy and Ethical Concerns
5. Applications and Future Directions
5.1. Applications
5.2. Future Research Directions
6. Conclusion
References

키워드

저자

• Qurat Ul Ain Aisha [ Department of IT Engineering, Sookmyung Women’s University, Seoul, 04310 ]
• Ahhyeon Lee [ Department of IT Engineering, Sookmyung Women’s University, Seoul, 04310, ]
• Byung-Gyu Kim [ Department of IT Engineering, Sookmyung Women’s University/Artificial Intelligence Innovation Research Center, Sookmyung Women’s University ] Corresponding Author
• Jiwoo Kang [ Department of IT Engineering, Sookmyung Women’s University/Artificial Intelligence Innovation Research Center, Sookmyung Women’s University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The Journal of Sciences and Innovation for Sustainable Peace(구 The journal of Hyojeong Academia)

• 간기

  반년간

• pISSN

  2982-9305

• 수록기간

  2023~2026

• 십진분류

  KDC 238 DDC 289