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About

Hedwig Eisenbarth is a researcher specializing in the intersection of affective computing, machine learning, and human-computer interaction, with a particular focus on automated emotion recognition from facial expressions. Their body of work addresses one of the field's most pressing challenges: accurately categorizing emotions when faces are partially obscured by coverings such as masks or sunglasses — a problem that gained heightened relevance during the global pandemic era. Eisenbarth's notable contributions include developing and evaluating machine learning classifiers that rival or complement human performance in emotion detection, exploring adversarial robustness through anti-attack methods for image-based emotion systems, and applying optimization techniques such as Particle Swarm Optimization for intelligent feature selection. More recently, their work has advanced attention-based deep learning architectures specifically designed to handle partially visible faces, pushing the boundaries of what intelligent systems can perceive in real-world conditions. With a growing citation record across publications from 2021 to 2023 — accumulating over 50 citations collectively — Eisenbarth's research is increasingly influential in shaping how robots and AI systems interpret human emotional cues in shared workspaces. Their comparative studies between human and machine classifiers offer particularly valuable benchmarks for the broader affective computing community.