Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots

Affective computing explores the development of systems and devices that can perceive, translate, process, and reproduce human emotion. It is an interdisciplinary field which includes computer science, psychology and cognitive science. An inspiration for the research is the ability to simulate empat...

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Main Authors: Carranza, Karmelo Antonio Lazaro R., Manalili, Joshua, Bugtai, Nilo T., Baldovino, Renann G.
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Published: Animo Repository 2019
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/1462
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2461/type/native/viewcontent
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-24612021-06-29T02:23:30Z Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots Carranza, Karmelo Antonio Lazaro R. Manalili, Joshua Bugtai, Nilo T. Baldovino, Renann G. Affective computing explores the development of systems and devices that can perceive, translate, process, and reproduce human emotion. It is an interdisciplinary field which includes computer science, psychology and cognitive science. An inspiration for the research is the ability to simulate empathy when communicating with computers or in the future robots. This paper explored the potential of facial expression tracking with deep learning to make chatbots more emotionally aware through developing a post-therapy session survey chatbot which responds depending on two inputs, interactant's response and facial expression. The developed chatbot summarizes emotional state of the user during the survey through percentages of the tracked facial expressions throughout the conversation with the chatbot. Facial expression tracking for happy, neutral, and hurt had 66.7%, 16.7%, and 56.7% tracking accuracy, respectively. Moreover, the developed program was tested to track expressions simultaneously per second. It can track 17 expressions with stationary subject and 14 expressions with non-stationary subject in a span of 30 seconds. © 2019 IEEE. 2019-11-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1462 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2461/type/native/viewcontent Faculty Research Work Animo Repository Emotion recognition Face perception Robots—Programming Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Emotion recognition
Face perception
Robots—Programming
Computer Sciences
spellingShingle Emotion recognition
Face perception
Robots—Programming
Computer Sciences
Carranza, Karmelo Antonio Lazaro R.
Manalili, Joshua
Bugtai, Nilo T.
Baldovino, Renann G.
Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
description Affective computing explores the development of systems and devices that can perceive, translate, process, and reproduce human emotion. It is an interdisciplinary field which includes computer science, psychology and cognitive science. An inspiration for the research is the ability to simulate empathy when communicating with computers or in the future robots. This paper explored the potential of facial expression tracking with deep learning to make chatbots more emotionally aware through developing a post-therapy session survey chatbot which responds depending on two inputs, interactant's response and facial expression. The developed chatbot summarizes emotional state of the user during the survey through percentages of the tracked facial expressions throughout the conversation with the chatbot. Facial expression tracking for happy, neutral, and hurt had 66.7%, 16.7%, and 56.7% tracking accuracy, respectively. Moreover, the developed program was tested to track expressions simultaneously per second. It can track 17 expressions with stationary subject and 14 expressions with non-stationary subject in a span of 30 seconds. © 2019 IEEE.
format text
author Carranza, Karmelo Antonio Lazaro R.
Manalili, Joshua
Bugtai, Nilo T.
Baldovino, Renann G.
author_facet Carranza, Karmelo Antonio Lazaro R.
Manalili, Joshua
Bugtai, Nilo T.
Baldovino, Renann G.
author_sort Carranza, Karmelo Antonio Lazaro R.
title Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
title_short Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
title_full Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
title_fullStr Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
title_full_unstemmed Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots
title_sort expression tracking with opencv deep learning for a development of emotionally aware chatbots
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/1462
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2461/type/native/viewcontent
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