'guten-learn-radio-image', 'class' => 'Guten_Learn_Customize_Control_Radio_Image', 'sanitize' => array( 'Guten_Learn_Customizer', 'sanitize_number' ), )); {"id":311,"date":"2022-05-12T14:35:00","date_gmt":"2022-05-12T11:35:00","guid":{"rendered":"https:\/\/aspida.ee.duth.gr\/?p=311"},"modified":"2022-11-30T15:47:46","modified_gmt":"2022-11-30T12:47:46","slug":"continuous-emotion-recognition-for-long-term-behavior-modeling-through-recurrent-neural-networks","status":"publish","type":"post","link":"https:\/\/aspida.ee.duth.gr\/en\/continuous-emotion-recognition-for-long-term-behavior-modeling-through-recurrent-neural-networks\/","title":{"rendered":"Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks"},"content":{"rendered":"
\n

\u2924<\/a><\/strong> Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks<\/h2><\/span>
One\u2019s internal state is mainly communicated through nonverbal cues, such as facial expressions, gestures and tone of voice, which in turn shape the corresponding emotional state. Hence, emotions can be effectively used, in the long term, to form an opinion of an individual\u2019s overall personality. The latter can be capitalized on in many human\u2013robot interaction (HRI) scenarios, such as in the case of an assisted-living robotic platform, where a human\u2019s mood may entail the adaptation of a robot\u2019s actions. To that end, we introduce a novel approach that gradually maps and learns the personality of a human, by conceiving and tracking the individual\u2019s emotional variations throughout their interaction. The proposed system extracts the facial landmarks of the subject, which are used to train a suitably designed deep recurrent neural network architecture. The above architecture is responsible for estimating the two continuous coefficients of emotion, i.e., arousal and valence, following the broadly known Russell\u2019s model. Finally, a user-friendly dashboard is created, presenting both the momentary and the long-term fluctuations of a subject\u2019s emotional state. Therefore, we propose a handy tool for HRI scenarios, where robot\u2019s activity adaptation is needed for enhanced interaction performance and safety.<\/div><\/div><\/section>\n\n\n\n

<\/p>\n\n\n\n

Authors<\/strong><\/span>
Ioannis Kansizoglou, Evangelos Misirlis, Konstantinos Tsintotas, Antonios Gasteratos<\/div><\/div><\/section>\n\n\n\n

<\/p>\n\n\n\n

\n
\n
Journal<\/strong><\/strong><\/span>
Technologies<\/div><\/div><\/section>\n<\/div>\n\n\n\n
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Publication Date<\/strong><\/strong><\/strong><\/span>
May 12th, 2022<\/div><\/div><\/section>\n<\/div>\n<\/div>\n\n\n\n

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