| Item type |
文献 / Documents(1) |
| 公開日 |
2021-09-30 |
| アクセス権 |
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|
アクセス権 |
open access |
| 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 |
| 出版社版DOI |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1109/ACCESS.2019.2936976 |
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|
言語 |
ja |
|
|
関連名称 |
10.1109/ACCESS.2019.2936976 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Facial Expression Recognition Based on Fusion Features of Center-Symmetric Local Signal Magnitude Pattern |
|
言語 |
en |
| タイトル別表記 |
|
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その他のタイトル |
FER Based on Fusion Features of CS-LSMP |
|
言語 |
en |
| 著者 |
Hu, Min
Yang, Chunjian
Zheng, Yaqin
Wang, Xiaohua
He, Lei
任, 福継
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Local feature descriptors play a fundamental and important role in facial expression recognition. This paper presents a new descriptor, Center-Symmetric Local Signal Magnitude Pattern (CS-LSMP), which is used for extracting texture features from facial images. CS-LSMP operator takes signal and magnitude information of local regions into account compared to conventional LBP-based operators. Additionally, due to the limitation of single feature extraction method and in order to make full advantages of different features, this paper employs CS-LSMP operator to extract features from Orientational Magnitude Feature Maps (OMFMs), Positive-and-Negative Magnitude Feature Maps (PNMFMs), Gabor Feature Maps (GFMs) and facial patches (eyebrows-eyes, mouths) for obtaining fused features. Unlike HOG, which only retains horizontal and vertical magnitudes, our work generates Orientational Magnitude Feature Maps (OMFMs) by expanding multi-orientations. This paper build two distinct feature maps by dividing local magnitudes into two groups, i.e., positive and negative magnitude feature maps. The generated Gabor Feature Maps (GFMs) are also grouped to reduce the computational complexity. Experiments on the JAFFE and CK+ facial expression datasets showed that the proposed framework achieved significant improvement and outperformed some state-of-the-art methods. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Facial expression recognition |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
center-symmetric local signal magnitude pattern |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
local representation |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
feature fusion |
| 書誌情報 |
en : IEEE Access
巻 7,
p. 118435-118445,
発行日 2019-08-22
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
21693536 |
| 出版者 |
|
|
出版者 |
IEEE |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ |
| EID |
|
|
識別子 |
363138 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
access_7_118435.pdf (8.03 MB)
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