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About

Sebastian Hommel is a researcher whose work lies at the intersection of computer vision, human-robot interaction, and affective computing. His primary research focus involves enabling machines to perceive and respond to human emotional and attentional states in real time, particularly within mobile and interactive platforms. Hommel’s major contributions center on the development of automatic, real-time systems for facial expression recognition and user attention estimation. His most cited work, “AAM based continuous facial expression recognition for face image sequences” (13 citations), pioneered a method using Active Appearance Models (AAMs) to map facial features onto the seven basic emotions, enabling continuous, real-time emotion classification. He further advanced this approach in “Realtime AAM based user attention estimation,” where he combined AAMs with Multilayer Perceptrons to estimate head pose and gaze direction. Hommel’s impact is demonstrated through a body of work that has accumulated over 40 citations, with key applications in mobile face detection and dialog system adaptation. Notably, his research on “Attention and Emotion Based Adaption of Dialog Systems” (8 citations) directly influenced the development of more responsive, human-aware conversational agents, showcasing his commitment to creating intuitive, emotionally intelligent human-machine interfaces.