| Item type |
デフォルトアイテムタイプ（フル）(1) |
| 公開日 |
2018-01-29 |
| タイトル |
|
|
タイトル |
畳み込みニューラルネットワークを用いた表情表現の獲得と顔特徴量の分析 |
|
言語 |
ja |
| タイトル |
|
|
タイトル |
Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks |
|
言語 |
en |
| 作成者 |
西銘, 大喜
遠藤, 聡志
當間, 愛晃
山田, 考治
赤嶺, 有平
Nishime, Taiki
Endo, Satoshi
Toma, Naruaki
Yamada, Koji
Akamine, Yuhei
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
facial expression |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
convolutional neural networks |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Facial expressions play an important role in communication as much as words. In facial expression recognition by human, it is difficult to uniquely judge, because facial expression has the sway of recognition by individual difference and subjective recognition. Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN) will be considered important. In this study, we carried out the facial expression recognition from facial expression images using CNN. In addition, we analysed CNN for understanding learned features and prediction results. Emotions we focused on are "happiness", "sadness", "surprise", "anger", "disgust", "fear" and "neutral". As a result, using 32286 facial expression images, have obtained an emotion recognition score of about 57%; for two emotions\n(Happiness, Surprise) the recognition score exceeded 70%, but Anger and Fear was less than 50%. In the analysis of CNN, we focused on the learning process, input and intermediate layer. Analysis of the learning progress confirmed that increased data can be recognized in the following order "happiness", "surprise", "neutral", "anger", "disgust", "sadness" and "fear". From the analysis result of the input and intermediate layer, we confirmed that the feature of the eyes and mouth strongly influence the facial expression recognition, and intermediate layer neurons had active patterns corresponding to facial expressions, and also these activate patterns do not respond to partial features of facial expressions. From these results, we concluded that CNN has learned the partial features of eyes and mouth from input, and recognize the facial expression using hidden layer units having the area corresponding to each facial expression. |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
論文 |
| 出版者 |
|
|
出版者 |
社団法人 人工知能学会 |
|
言語 |
ja |
| 出版者 |
|
|
出版者 |
THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE |
|
言語 |
en |
| 言語 |
|
|
言語 |
jpn |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 識別子 |
|
|
識別子 |
http://hdl.handle.net/20.500.12000/37607 |
|
識別子タイプ |
HDL |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1527/tjsai.F-H34 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
info:doi/10.1527/tjsai.F-H34 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
1346-0714 |
| 収録物名 |
|
|
収録物名 |
人工知能学会論文誌 |
|
言語 |
ja |
| 書誌情報 |
巻 32,
号 5,
p. none,
発行日 2017-09-01
|
Vol37no5F.pdf
