Liangfei Zhang

Papers

Total Citations

145

H-Index

About

Liangfei Zhang is a leading researcher in affective computing and computer vision, with a primary focus on micro-expression recognition—a challenging domain that seeks to decode fleeting, involuntary facial movements that reveal genuine human emotions. His most cited work, the 2022 paper "Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition" (145 citations), introduces a novel transformer architecture that captures both local and global spatio-temporal dependencies, significantly advancing the field beyond earlier handcrafted feature-based methods. This contribution addresses the fundamental difficulties of micro-expression analysis—namely, their brief duration and low intensity—by leveraging deep learning to model subtle motion patterns. Zhang's research has been instrumental in pushing the boundaries of automated emotion inference, with applications in psychology, security, and human-computer interaction. His work stands out for its innovative integration of transformer models into micro-expression recognition, achieving state-of-the-art performance and inspiring subsequent studies in spatio-temporal affective computing. With a growing citation impact, Zhang continues to shape how machines perceive and interpret concealed emotional states.