Christian Lang

Papers

Total Citations

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About

Christian Lang’s research lies at the intersection of computer vision, pattern recognition, and human-robot interaction, with a focused interest in how machines can interpret subtle, non-verbal human cues. His key contributions center on the analysis of facial communicative signals (FCSs)—including head gestures, eye gaze, and facial expressions—to enhance the naturalness of human-robot dialogue. In his foundational 2012 work, “Facial Communicative Signals,” Lang established a framework for understanding these cues as critical feedback mechanisms in social interaction. He advanced this line of inquiry in his 2013 paper, where he introduced a discriminative video subsequence selection method for interpreting the valence of facial signals in real-time human-robot exchanges. Though his citation counts (7 and 4, respectively) are modest, Lang’s work is notable for its early, systematic approach to a challenging problem: enabling robots to read the emotional and communicative intent behind fleeting facial movements. His contributions are particularly relevant for researchers developing more responsive, socially aware robotic systems.