Emotion Detection in AI and Computer Vision: A Comprehensive Survey of Methods and Challenges
Keywords:
Artificial Intelligence (AI), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Learning (DL), Emotion detection, Machine Learning (ML), Natural Language Processing (NLP), Support Vector Machine (SVM)Abstract
This survey delves into the evolving landscape of emotion detection through the lens of Artificial Intelligence (AI) and Computer Vision. It examines various modalities such as facial expressions, body gestures, voice, and physiological signals, providing a comprehensive overview of state-of-the-art methodologies and recent advancements in the field. A significant emphasis is placed on multimodal approaches, which integrate multiple data sources to enhance the accuracy and robustness of emotion recognition systems.
The survey also addresses critical challenges, including dataset biases and cross-cultural variations that can affect the performance and generalizability of AI models. By highlighting these issues, the survey underscores the importance of developing more inclusive and representative datasets to improve the reliability of emotion detection systems across diverse populations.
Comparative analyses within the survey offer valuable insights into the strengths and weaknesses of different techniques, helping to identify the most promising avenues for future research. These analyses shed light on current capabilities and outline potential directions for innovation and improvement in AI-driven emotion recognition.
Aimed at researchers, practitioners, and enthusiasts, this survey serves as a concise and informative resource for understanding AI's current state and future potential in the realm of emotion detection in Computer Vision. It provides a foundational understanding for those looking to explore or contribute to this rapidly advancing field.
