Jiuqiang Han

Papers

Total Citations

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About

Jiuqiang Han’s research lies at the intersection of computer vision, pattern recognition, and human-robot interaction, with a particular emphasis on enabling machines to perceive and engage with complex, real-world environments. His work on robust face recognition addresses one of the most persistent challenges in autonomous robotics: identifying individuals under occlusion or disguise. In his highly cited 2012 paper, Han advanced the sparse representation-based classification (SRC) framework, demonstrating how an over-complete dictionary of training samples could be used to reliably recognize faces even when the subject is wearing a disguise—a critical capability for security and service robots. This contribution, which has garnered 5 citations, laid important groundwork for more resilient vision systems. More recently, Han has explored the cognitive and perceptual demands of incomplete-information games. His 2021 paper on a human-robot interactive Mahjong system, which uses a convolutional neural network for visual recognition, showcases how deep learning can be applied to complex tabletop games that are far more unpredictable than Go or chess. With 2 citations, this work highlights Han’s commitment to bridging the gap between theoretical computer vision and tangible, playful human-robot collaboration.