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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Real-Time Gender Based Behavior System for Human-Robot Interaction
verfasst von : Pierluigi Carcagnì, Dario Cazzato, Marco Del Coco, Marco Leo, Giovanni Pioggia, Cosimo Distante
Erschienen in: Social Robotics
Verlag: Springer International Publishing
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This work introduces a real-time system able to lead humanoid robot behavior depending on the gender of the interacting person. It exploits Aldebaran NAO humanoid robot view capabilities by applying a gender prediction algorithm based on the face analysis. The system can also manage multiple persons at the same time, recognizing if the group is composed by men, women or is a mixed one and, in the latter case, to know the exact number of males and females, customizing its response in each case. The system can allow for applications of human-robot interaction requiring an high level of realism, like rehabilitation or artificial intelligence.