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About

Guoying Zhao is a pioneering researcher in affective computing, specializing in micro-expression recognition and spontaneous gesture analysis for emotional stress detection. Her work addresses the challenge of inferring concealed human emotions through subtle, involuntary facial movements and body language. Zhao’s most cited paper, “Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition” (2022, 145 citations), introduces a novel transformer architecture that captures both short- and long-range spatial-temporal dependencies, significantly advancing the recognition of micro-expressions—fleeting, low-intensity facial cues critical for detecting true emotions. She also developed the first micro-gesture dataset for emotional stress state recognition (“Analyze Spontaneous Gestures for Emotional Stress State Recognition,” 2019, 42 citations), extending emotion analysis beyond traditional facial and speech cues to include body gestures, a key non-verbal communication channel. Zhao’s editorial work on “Human Behaviour Analysis ‘In-the-Wild’” (2019) underscores her commitment to real-world applications, from human-computer interaction to security. Her contributions have established her as a leader in affective computing, bridging the gap between subtle human behavior and machine understanding.