WEKO3
アイテム
Personal Authentication for Periocular Region in Thermal and Visible Light Images by Using CNN
https://ipsj.ixsq.nii.ac.jp/records/234273
https://ipsj.ixsq.nii.ac.jp/records/2342731a25c1be-43e5-4a73-bdce-2b460afd3eb1
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL6505008.pdf (1.2 MB)
2026年5月15日からダウンロード可能です。
|
Copyright (c) 2024 by the Information Processing Society of Japan
|
|
| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-05-15 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Personal Authentication for Periocular Region in Thermal and Visible Light Images by Using CNN | |||||||||||||
| タイトル | ||||||||||||||
| 言語 | en | |||||||||||||
| タイトル | Personal Authentication for Periocular Region in Thermal and Visible Light Images by Using CNN | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | [一般論文] biometrics, periocular recognition, thermography | |||||||||||||
| 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 | |||||||||||||
| 著者所属 | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属 | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属 | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属 | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属(英) | ||||||||||||||
| en | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属(英) | ||||||||||||||
| en | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属(英) | ||||||||||||||
| en | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者所属(英) | ||||||||||||||
| en | ||||||||||||||
| The University of Electro-Communications | ||||||||||||||
| 著者名 |
Takumi, Nakamura
× Takumi, Nakamura
× Shusuke, Kawamura
× Hiroshi, Yoshiura
× Masatsugu, Ichino
|
|||||||||||||
| 著者名(英) |
Takumi, Nakamura
× Takumi, Nakamura
× Shusuke, Kawamura
× Hiroshi, Yoshiura
× Masatsugu, Ichino
|
|||||||||||||
| 論文抄録 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | Personal authentication based on the periocular region can be performed even when the person's mouth and nose are hidden by a face mask. However, using visible light images for periocular recognition is problematic because recognition accuracy is affected by changes in the lighting conditions. We have developed a method for periocular recognition that overcomes this problem by using thermal images, which are less affected by changes in lighting conditions, in addition to visible light images. In this paper, we propose a method using both thermal and visible light images for periocular recognition based on features obtained by CNN. In addition, our method uses deep metric learning to deal with persons who are not included in the training data. To evaluate the accuracy of the proposed method under unstable conditions, we conducted recognition experiments using images of 83 subjects obtained from the USTC-NVIE database, which contains visible light and thermal images taken simultaneously under various lighting conditions and with various facial expressions. The experimental results show that using both visible light and thermal images achieves higher recognition accuracy than using only visible light images. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.396 ------------------------------ |
|||||||||||||
| 論文抄録(英) | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | Personal authentication based on the periocular region can be performed even when the person's mouth and nose are hidden by a face mask. However, using visible light images for periocular recognition is problematic because recognition accuracy is affected by changes in the lighting conditions. We have developed a method for periocular recognition that overcomes this problem by using thermal images, which are less affected by changes in lighting conditions, in addition to visible light images. In this paper, we propose a method using both thermal and visible light images for periocular recognition based on features obtained by CNN. In addition, our method uses deep metric learning to deal with persons who are not included in the training data. To evaluate the accuracy of the proposed method under unstable conditions, we conducted recognition experiments using images of 83 subjects obtained from the USTC-NVIE database, which contains visible light and thermal images taken simultaneously under various lighting conditions and with various facial expressions. The experimental results show that using both visible light and thermal images achieves higher recognition accuracy than using only visible light images. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.396 ------------------------------ |
|||||||||||||
| 書誌レコードID | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AN00116647 | |||||||||||||
| 書誌情報 |
情報処理学会論文誌 巻 65, 号 5, 発行日 2024-05-15 |
|||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 1882-7764 | |||||||||||||
| 公開者 | ||||||||||||||
| 言語 | ja | |||||||||||||
| 出版者 | 情報処理学会 | |||||||||||||
