| Item type |
SIG Technical Reports(1) |
| 公開日 |
2016-11-02 |
| タイトル |
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タイトル |
Mixed Features for Face Detection in Thermal Image |
| タイトル |
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言語 |
en |
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タイトル |
Mixed Features for Face Detection in Thermal Image |
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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_18gh |
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資源タイプ |
technical report |
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Kyushu University |
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Kyushu University |
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Kyushu University |
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Kyushu University |
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Kyushu University |
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Kyushu University |
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en |
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Kyushu University |
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en |
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Kyushu University |
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en |
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Kyushu University |
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en |
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Kyushu University |
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en |
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Kyushu University |
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en |
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Kyushu University |
| 著者名 |
Chao, Ma
Ngo, Thanh Trung
Hideaki, Uchiyama
Hajime, Nagahara
Atsushi, Shimada
Rin-ichiro, Taniguchi
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| 著者名(英) |
Chao, Ma
Ngo, Thanh Trung
Hideaki, Uchiyama
Hajime, Nagahara
Atsushi, Shimada
Rin-ichiro, Taniguchi
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
An infrared camera is able to capture temperature distribution as an infrared (IR) image. It is a powerful tool in human related applications, such as human face recognition in complex illumination and fever screening in public places relying on facial temperature. Since facial temperature is almost constant, it is easy to find the facial region on an IR image. However, a simple temperature thresholding is not always working for detecting face stably. It is a standard for face detection to use Adaboost with local features such as Haar-like, MB-LPB, and HoG in visible image. However, there are very few research works using these local features in IR domain. In this paper, we propose an AdaBoost based training method to mix these local features for face detection in IR domain. In experiment, we captured a dataset of 20 people including 14 males and 6 females with variations of 10 different distances, 21 poses, and with/without glasses. We showed the proposed mixed features has an advantage over all of the regular local features using leave-one-out cross-validation. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
An infrared camera is able to capture temperature distribution as an infrared (IR) image. It is a powerful tool in human related applications, such as human face recognition in complex illumination and fever screening in public places relying on facial temperature. Since facial temperature is almost constant, it is easy to find the facial region on an IR image. However, a simple temperature thresholding is not always working for detecting face stably. It is a standard for face detection to use Adaboost with local features such as Haar-like, MB-LPB, and HoG in visible image. However, there are very few research works using these local features in IR domain. In this paper, we propose an AdaBoost based training method to mix these local features for face detection in IR domain. In experiment, we captured a dataset of 20 people including 14 males and 6 females with variations of 10 different distances, 21 poses, and with/without glasses. We showed the proposed mixed features has an advantage over all of the regular local features using leave-one-out cross-validation. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA11131797 |
| 書誌情報 |
研究報告コンピュータビジョンとイメージメディア（CVIM）
巻 2016-CVIM-204,
号 1,
p. 1-5,
発行日 2016-11-02
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8701 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-CVIM16204001.pdf (1.1 MB)
