• Title/Summary/Keyword: Synthetic Emotion Recognition

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KOBIE: A Pet-type Emotion Robot (KOBIE: 애완형 감성로봇)

  • Ryu, Joung-Woo;Park, Cheon-Shu;Kim, Jae-Hong;Kang, Sang-Seung;Oh, Jin-Hwan;Sohn, Joo-Chan;Cho, Hyun-Kyu
    • The Journal of Korea Robotics Society
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    • v.3 no.2
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    • pp.154-163
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    • 2008
  • This paper presents the concept for the development of a pet-type robot with an emotion engine. The pet-type robot named KOBIE (KOala roBot with Intelligent Emotion) is able to interact with a person through touch. KOBIE is equipped with tactile sensors on the body for interaction with a person through recognition of his/her touching behaviors such as "Stroke","Tickle","Hit". We have covered KOBIE with synthetic fur fabric in order to can make him/her feel affection as well. KOBIE is able to also express an emotional status that varies according to the circumstances under which it is presented. The emotion engine of KOBIE's emotion expression system generates an emotional status in an emotion vector space which is associated with a predefined needs and mood models. In order to examine the feasibility of our emotion expression system, we verified a changing emotional status in our emotion vector space by a touching behavior. We specially examined the reaction of children who have interacted with three kind of pet-type robots: KOBIE, PARO, AIBO for roughly 10 minutes to investigate the children's preference for pet-type robots.

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Image Recognition based on Adaptive Deep Learning (적응적 딥러닝 학습 기반 영상 인식)

  • Kim, Jin-Woo;Rhee, Phill-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.1
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    • pp.113-117
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    • 2018
  • Human emotions are revealed by various factors. Words, actions, facial expressions, attire and so on. But people know how to hide their feelings. So we can not easily guess its sensitivity using one factor. We decided to pay attention to behaviors and facial expressions in order to solve these problems. Behavior and facial expression can not be easily concealed without constant effort and training. In this paper, we propose an algorithm to estimate human emotion through combination of two results by gradually learning human behavior and facial expression with little data through the deep learning method. Through this algorithm, we can more comprehensively grasp human emotions.