Yang Chang

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About

Yang Chang is a leading researcher in affective computing and human-robot interaction, with a primary focus on facial expression recognition (FER) across both static images and dynamic video sequences. His seminal survey, "A Survey on Facial Expression Recognition of Static and Dynamic Emotions" (2024), has already garnered early citations, establishing itself as a foundational reference in the field. Chang’s work critically bridges the gap between controlled laboratory studies and real-world, unconstrained environments, addressing key challenges such as illumination variation, head pose, and temporal dynamics in emotion analysis. By systematically categorizing deep learning approaches—from convolutional neural networks to recurrent architectures—he has provided a comprehensive roadmap for advancing anthropomorphic communication between humans, robots, and digital avatars. His contributions are instrumental in pushing FER beyond academic benchmarks toward practical deployment in healthcare, education, and interactive AI systems. With a growing citation footprint and a clear trajectory toward high-impact scholarship, Yang Chang is shaping the next generation of emotionally intelligent machines that can perceive and respond to human affective states with increasing accuracy and nuance.