Papers
7
Total Citations
608
H-Index
6
About
Wanjuan Su is a leading researcher in affective computing and human-robot interaction (HRI), with a focus on enabling machines to perceive, recognize, and respond to human emotional states. Her work centers on developing deep learning and fuzzy logic frameworks for multimodal emotion recognition, particularly through facial expressions, speech, and gestures. Su's most influential contribution is the Softmax regression-based deep sparse autoencoder network for facial emotion recognition (198 citations), which set a benchmark for robust feature extraction in HRI. She further advanced the field with a two-layer fuzzy multiple random forest model for speech emotion recognition (193 citations) and the Weight-Adapted Convolution Neural Network (WACNN, 89 citations), which optimizes CNN performance by avoiding local optima and accelerating convergence. Her innovative integration of fuzzy C-means clustering with deep neural networks for emotional intention understanding (59 citations) and adaptive feature selection for dynamic emotion recognition (54 citations) demonstrates her commitment to real-time, adaptive systems. With over 600 total citations, Su's research has significantly improved the emotional intelligence of robots, making them more intuitive and responsive in human-centered applications.
Research Focus
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