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Emotion-Based Painting Image Display System

Taemin Lee1, Dongwann Kang2, Kyunghyun Yoon1, Sanghyun Seo3,*

1 Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
2 Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.
3 College Art & Technology, Chung-Ang University, Anseong -si, Kyunggi-do, Republic of Korea

* Corresponding Author: Sanghyun Seo, email

Intelligent Automation & Soft Computing 2020, 26(1), 181-192. https://doi.org/10.31209/2019.100000139

Abstract

As mobile devices have tremendously developed, people can now get sensor data easily. These data are not only physical data such as temperature, humidity, gravity, acceleration, etc. but also human health data such as blood pressure, heart pulse rate, etc. With this information, Internet of Things (IoT) technology has provided many systems to support human health care. Systems for human health care support physical health care like checking blood pressure, pulse rate, etc. However, the demand for physical health care as well as mental health care is increasing. So, a system, which automatically recommends a painting to users based on their feeling, is proposed in this paper. Using a smartphone application, users take a self-portrait. Then, the application reads the user’s facial expression, and obtains an Arousal-Valence (A.V.) emotion value. Also, the application has a database of paintings with A.V. value in advance. To create this database, we extracted many features from various paintings and estimated their A.V. value using regression analysis. When users reach home, the application detects it automatically using GPS information, and shows the painting that best suits the user’s emotion, based on the extracted A.V. value. Thereby, users can get a feeling of relaxation by admiring the painting.

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Cite This Article

T. Lee, D. Kang, K. Yoon and S. Seo, "Emotion-based painting image display system," Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 181–192, 2020.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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