| Title: | GS2-2 Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 224-227 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS2-2 |
| Author(s): | Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine |
| Publication Date: | January 29, 2016 |
| Keywords: | facial expression recognition, convolutional neural networks, deep learning, feature learning |
| Abstract: | In this study, we carried out the facial expression recognition from facial expression dataset using Convolutional Neural Networks (CNN). In addition, we analyzed intermediate outputs of CNN. As a result, we have obtained a emotion recognition score of about 58%; two emotions (Happiness, Surprise) recognition score was about 70%. We also confirmed that specific unit of intermediate layer have learned the feature about Happiness. This paper details these experiments and investigations regarding the influence of CNN learning from facial expression. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS2-2.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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