Xiaojie Fang
Papers
1
Total Citations
13
H-Index
1
About
Xiaojie Fang is a researcher whose work lies at the intersection of computer vision and affective computing, with a primary focus on facial expression recognition (FER). His key contributions address the challenge of accurately capturing subtle facial features in real-world applications, such as driver fatigue monitoring and social robotics. Fang’s most cited paper, "Lightweight Deep Learning Model For Facial Expression Recognition" (2019, 13 citations), introduces an efficient neural network architecture that balances high accuracy with computational lightness—a critical advancement for deploying FER systems on resource-constrained devices. This work has been recognized for its practical impact, enabling more responsive and portable emotion-aware technologies. By prioritizing both performance and efficiency, Fang’s research bridges the gap between theoretical deep learning models and their deployment in sensitive, real-time environments. His efforts continue to influence the development of lightweight, robust systems for human-computer interaction, making emotion recognition more accessible and reliable across diverse fields from healthcare to autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Lightweight Deep Learning Model For Facial Expression Recognition13 citations · 2019