Papers
14
Total Citations
937
H-Index
10
About
Weihua Cao is a leading researcher in affective computing and human-robot interaction, whose work bridges machine perception and emotional intelligence. His core research areas include speech emotion recognition, facial expression analysis, and multimodal human-robot communication systems. Cao's most impactful contribution is the development of the FEER-HRI system (179 citations), a four-layer framework that enables robots not only to recognize human emotions but also to generate adaptive facial expressions. His work on the WACNN model (89 citations) advanced facial expression recognition by optimizing convolutional neural networks to avoid local optima and accelerate convergence. In speech emotion recognition, his feature selection and extreme learning machine decision tree approach (260 citations) set a benchmark, while his improved brain emotion learning model (169 citations) introduced biologically inspired architectures. Cao has also pioneered EEG-based emotion recognition using empirical mode decomposition (80 citations) and proposed multimodal systems integrating speech, facial expressions, and gestures (38 citations). His initiative service models for robots, including a drinking service robot using fuzzy analytical hierarchy process (10 citations), demonstrate practical applications in assistive robotics. With over 900 total citations, Cao's work has fundamentally shaped how machines perceive and respond to human emotions, making him a pivotal figure in creating emotionally intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2A facial expression emotion recognition based human-robot interaction system179 citations · 2017
- 3Speech emotion recognition based on an improved brain emotion learning model169 citations · 2018
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- 7A multimodal emotional communication based humans-robots interaction system38 citations · 2016
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- 9Proposal of initiative service model for service robot11 citations · 2017
- 10