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• W2915051973 abstract "Lie detection is a necessary skill for a variety of social professions, including teachers, reporters, therapists, and law enforcement officers. Autonomous system and robots should acquire such skill to support professionals in numerous working contexts. Inspired by literature on human-human interaction, this work investigates whether the behavioral cues associated to lying - including eye movements and response temporal features - are apparent also during human-humanoid interaction and can be leveraged by the robot to detect deception. The results highlight strong similarities in the lying behavior toward humans and the robot. Further, the study proposes an implementation of a machine learning algorithm that can detect lies with an accuracy of 75%, when trained with a dataset collected during human-human and human robot interaction. Consequently, this work proposes a technological solution for humanoid interviewers that can be trained with knowledge about lie detection and reuse it to counteract deception." @default.
• W2915051973 created "2019-02-21" @default.
• W2915051973 creator A5002153617 @default.
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• W2915051973 date "2018-11-01" @default.
• W2915051973 modified "2023-10-03" @default.
• W2915051973 title "Can a Humanoid Robot Spot a Liar?" @default.
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• W2915051973 doi "https://doi.org/10.1109/humanoids.2018.8624992" @default.
• W2915051973 hasPublicationYear "2018" @default.
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