Matthieu Labeau

University of Southern California, Télécom Paris

Papers

Total Citations

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About

Matthieu Labeau is a researcher at the intersection of computer vision and conversational AI, whose work bridges the technical and social dimensions of human-machine interaction. His early contributions to computer vision include a pioneering system for **3D facial landmark tracking and facial expression recognition** (2013, 14 citations), which combined a generic 3D face model with head tracking and 2D trackers to enable reliable, real-time expression analysis during natural interactions—a foundational step for affective computing. More recently, Labeau has focused on the emerging field of **socio-conversational systems**, a domain encompassing chatbots, vocal assistants, and social robots. His influential 2022 paper (7 citations) identifies three key challenges at the crossroads of dialogue systems and social interaction, arguing that truly human-like conversation requires treating the social nature of interaction as a core design principle, not an afterthought. This work positions him as a critical voice in shaping how future AI systems understand and respond to human social cues. Labeau’s career reflects a rare ability to move from low-level tracking algorithms to high-level interaction design, making him a compelling figure for students interested in building machines that not only see but also connect.