Papers
2
Total Citations
9
H-Index
2
About
Deyun Chen’s research lies at the intersection of deep learning, robotics, and human-computer interaction, with a particular focus on enabling machines to perceive and respond to human emotional cues. Chen’s most cited work, “Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System” (2022, 6 citations), tackles a critical challenge in social robotics: the accurate and robust recognition of human facial expressions. By enhancing the AlexNet convolutional neural network, Chen’s method improves feature extraction and reduces misclassification from noisy or mislabeled data, directly advancing the reliability of robot-mediated interactions. This contribution is foundational for developing more empathetic and responsive service robots. Earlier work, “Autonomous Navigation Based on a Novel Topological Map” (2009, 3 citations), demonstrates Chen’s versatility by addressing spatial intelligence. This research proposed a hybrid topological map that fuses laser rangefinder data with visual scale-invariant features, enabling mobile robots to navigate autonomously in large, unknown indoor environments. Together, these papers highlight Chen’s dual focus on perceptual intelligence and autonomous mobility—two pillars of modern robotics. With a career spanning foundational navigation techniques to cutting-edge deep learning for affective computing, Deyun Chen’s work continues to shape how robots understand both their physical surroundings and the people within them.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous Navigation Based on a Novel Topological Map3 citations · 2009