Michael Ingleby

Papers

Total Citations

108

H-Index

About

Michael Ingleby’s research lies at the intersection of affective computing, human-robot interaction, and physiological sensing. His pioneering work explores how machines can perceive and adapt to human emotional states—a critical step toward achieving artificial sociability. In his most cited paper (2006, 76 citations), Ingleby demonstrated that infrared measurement of facial skin temperature variations could be used for automated facial expression classification and affect interpretation, offering a robust alternative to traditional vision-based systems that struggle with variable lighting and occlusion. He later advanced this line of inquiry in a 2016 paper (32 citations) that addressed key challenges in automating affect and arousal assessment for clinical diagnostics and affect-aware robotics. Ingleby’s contributions are notable for pushing beyond conventional facial expression analysis by integrating thermal imaging, thereby enabling more reliable, dynamic assessment of emotional states. His work has significant implications for psychology, psychiatry, and the development of socially intelligent autonomous systems. For students and researchers, Ingleby’s research offers a compelling example of how physiological signals can bridge the gap between human emotion and machine understanding.