Wen-Huang Cheng
Papers
2
Total Citations
29
H-Index
2
About
Wen-Huang Cheng is a leading researcher in affective computing and computer vision, with a focus on bridging human emotional states and intelligent machine perception. His work centers on developing deep learning frameworks for emotion recognition from physiological signals, particularly galvanic skin response (GSR). In his highly cited 2020 paper, Cheng proposed a novel deep hybrid neural network architecture that achieves robust emotion classification by capturing both temporal and spectral features from GSR data—a contribution that has garnered 26 citations and established a foundation for non-invasive affective computing systems. Beyond emotion recognition, Cheng addresses practical challenges in scene text detection for robotic navigation. His 2021 work introduces a memory-efficient "re-attention" mechanism that selectively refines uncertain regions in segmentation-based text detection, reducing computational overhead while maintaining accuracy. This innovation is critical for real-time applications in autonomous systems. Cheng’s research demonstrates a consistent commitment to making AI more human-aware and resource-efficient, with potential impacts spanning healthcare, human-computer interaction, and intelligent robotics. His work continues to influence how machines interpret both textual environments and human emotional signals.
Research Focus
Key Achievements
Top Papers
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