| Item type |
FIT(1) |
| 公開日 |
2010-08-20 |
| タイトル |
|
|
言語 |
en |
|
タイトル |
H-036 Subtle Facial Expression Recognition Based on Expression Category-dependent Motion Magnification |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| 著者所属(英) |
|
|
|
en |
|
|
Tokyo University |
| 著者所属(英) |
|
|
|
en |
|
|
Nippon Telegraph and Telephone Corporation |
| 著者所属(英) |
|
|
|
en |
|
|
Nippon Telegraph and Telephone Corporation |
| 著者所属(英) |
|
|
|
en |
|
|
Nippon Telegraph and Telephone Corporation |
| 著者所属(英) |
|
|
|
en |
|
|
Nippon Telegraph and Telephone Corporation |
| 著者所属(英) |
|
|
|
en |
|
|
Nippon Telegraph and Telephone Corporation |
| 著者所属(英) |
|
|
|
en |
|
|
Tokyo University |
| 著者名(英) |
Su, Lumei
Kumano, Shiro
Otsuka, Kazuhiro
Mikami, Dan
Yamato, Junji
Maeda, Eisaku
Sato, Yoichi
|
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Previous subtle facial expression recognition studies have primarily focused on extracting the subtle changes of facial expression but not classifying the extracted subtle expression into categories such as anger and happiness. In this paper, we propose a novel method based on expression category-dependent motion magnification to recognize subtle facial expressions. Because it's difficult to discriminate subtle expressions directly from raw subtle facial deformation data, we use expression category-dependent motion magnification to reliably transform subtle facial expressions into the corresponding exaggerated facial expressions. The magnified facial expressions become much more discriminable and we can infer the expression category of the subtle expression from them. Experiments using our database showed that the proposed method has a promising performance on subtle expression recognition, especially in the early stage of making facial expression. |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA1242354X |
| 書誌情報 |
情報科学技術フォーラム講演論文集
巻 9,
号 3,
p. 209-212,
発行日 2010-08-20
|
| 出版者 |
|
|
言語 |
ja |
|
出版者 |
情報処理学会 |
KJ00006917266.pdf (449.2 kB)
