Papers
39
Total Citations
1,418
H-Index
16
About
Luefeng Chen is a prominent researcher specializing in affective computing, human-robot interaction (HRI), and intelligent machine learning systems. His work sits at the intersection of emotion recognition, deep learning, and fuzzy logic, with a sustained focus on enabling robots to perceive, interpret, and respond meaningfully to human emotional states. Chen's most celebrated contributions include the development of innovative architectures for facial and speech emotion recognition. His softmax regression-based deep sparse autoencoder (2017, 198 citations) and two-layer fuzzy multiple random forest for speech emotion recognition (2019, 193 citations) represent landmark advances in multimodal affective computing. A defining achievement is his FEER-HRI system, which not only classifies human emotions but enables robots to generate corresponding facial expressions—creating genuinely empathetic machine behavior. Beyond recognition, Chen has tackled the deeper challenge of emotional intention understanding, proposing fuzzy support vector regression models and fuzzy deep neural networks that bridge emotion identification with behavioral adaptation. His K-Means clustering-based kernel canonical correlation analysis (2022, 106 citations) further advances multimodal fusion strategies in HRI contexts. Collectively, Chen's papers have accumulated over 1,000 citations, reflecting substantial influence on the field. His research has meaningfully advanced the goal of socially intelligent robots capable of nuanced human-centered interaction.
Research Focus
Key Achievements
Top Papers
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- 3A facial expression emotion recognition based human-robot interaction system179 citations · 2017
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