Utilization of Computer Vision Technology for Human Emotion Detection and Recognition in the Development of a More Responsive Human-Computer Interaction System

Abstract

In recent years, the integration of emotion recognition technologies into Human-Computer Interaction (HCI) systems has emerged as a critical advancement in the pursuit of more responsive and human-centered digital experiences. This study investigates the utilization of computer vision technology for detecting and recognizing human emotions to enhance the adaptability and empathy of HCI systems. Employing a qualitative research approach through a structured literature review and library research method, this paper synthesizes findings from selected peer-reviewed studies published over the past five years. The review highlights key developments in deep learning-based emotion recognition, with particular emphasis on facial expression analysis, body gesture interpretation, and multimodal data integration. Advanced computer vision techniques, such as convolutional neural networks (CNNs) and transformer-based models, are shown to significantly improve accuracy in identifying emotional states. Additionally, this study discusses current challenges, including cultural biases, data privacy concerns, and real-world implementation limitations. It also explores the ethical implications of emotion-aware systems and underscores the necessity for inclusive, transparent, and context-aware AI design. By analyzing and interpreting these trends and challenges, this research offers valuable insights for future innovations in emotion-sensitive HCI. The study concludes that emotionally intelligent interfaces, when ethically developed and inclusively trained, can redefine digital interactions across various domains such as education, healthcare, and customer service. Recommendations are proposed for future research to address existing gaps and enhance the practical applicability of emotion recognition systems.