Journal of Robotics, Networking and Artificial Life

Volume 3, Issue 1, June 2016, Pages 9 - 12

Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks

Authors
Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine
Corresponding Author
Taiki Nishime
Available Online 1 June 2016.
DOI
10.2991/jrnal.2016.3.1.3How to use a DOI?
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.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
3 - 1
Pages
9 - 12
Publication Date
2016/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2016.3.1.3How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Taiki Nishime
AU  - Satoshi Endo
AU  - Koji Yamada
AU  - Naruaki Toma
AU  - Yuhei Akamine
PY  - 2016
DA  - 2016/06/01
TI  - Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 9
EP  - 12
VL  - 3
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2016.3.1.3
DO  - 10.2991/jrnal.2016.3.1.3
ID  - Nishime2016
ER  -