Da-Mi Jeong

Papers

Total Citations

302

H-Index

About

Da-Mi Jeong is a leading researcher in artificial intelligence and affective computing, with a primary focus on advancing facial expression recognition (FER) technology. Her most influential work, the 2019 paper "Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure," has garnered over 300 citations, establishing her as a key contributor to human-computer interaction. In this landmark study, Jeong pioneered a hierarchical deep neural network architecture that dramatically improves the accuracy and efficiency of recognizing human emotions from visual cues, addressing the critical challenge of understanding emotional states in real-time AI systems. Her research bridges the gap between raw visual data and meaningful emotional interpretation, enabling more natural and responsive AI interactions. Beyond this core contribution, Jeong's work explores the broader implications of emotion-aware technology, from assistive robotics to mental health monitoring. With her innovative approach to deep learning structures and her demonstrated impact through high citation counts, Da-Mi Jeong continues to shape how machines perceive and respond to human emotional expressions, making her a vital figure in the evolution of empathetic artificial intelligence.