Yifeng Zhao

Harbin University of Science and Technology

Papers

Total Citations

H-Index

About

Yifeng Zhao is a researcher whose work sits at the intersection of computer vision, human–robot interaction, and deep learning. His primary research focus is on enhancing the perceptual capabilities of intelligent robotic systems, particularly through improved facial expression recognition. In his most-cited work, Zhao addresses a critical bottleneck in human–robot interaction: the inability of traditional convolutional neural networks to robustly extract subtle emotional features from faces, compounded by the problem of mislabeled training samples. He proposed a novel method leveraging an improved AlexNet architecture, which significantly boosts recognition accuracy and robustness. This contribution has been recognized with 6 citations, marking it as a foundational piece for researchers working on more empathetic and responsive social robots. Zhao’s work is notable for its practical orientation—bridging algorithmic innovation with real-world deployment in robot intelligent interactive systems. By tackling both feature extraction inefficiencies and label noise, he has helped pave the way for machines that can better understand and respond to human emotional states, a critical step toward seamless human–robot collaboration.