| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2024-05-24 |
| タイトル |
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タイトル |
Texture-Guided Transfer Learning for Low-Quality Face Recognition |
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言語 |
en |
| 著者 |
Zhang, Meng
Liu, Rujie
Deguchi, Daisuke
Murase, Hiroshi
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| アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
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権利情報 |
“© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” |
|
言語 |
en |
| 内容記述 |
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内容記述タイプ |
Abstract |
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内容記述 |
Although many advanced works have achieved significant progress for face recognition with deep learning and large-scale face datasets, low-quality face recognition remains a challenging problem in real-word applications, especially for unconstrained surveillance scenes. We propose a texture-guided (TG) transfer learning approach under the knowledge distillation scheme to improve low-quality face recognition performance. Unlike existing methods in which distillation loss is built on forward propagation; e.g., the output logits and intermediate features, in this study, the backward propagation gradient texture is used. More specifically, the gradient texture of low-quality images is forced to be aligned to that of its high-quality counterpart to reduce the feature discrepancy between the high- and low-quality images. Moreover, attention is introduced to derive a soft-attention (SA) version of transfer learning, termed as SA-TG, to focus on informative regions. Experiments on the benchmark low-quality face DB’s TinyFace and QMUL-SurFace confirmed the superiority of the proposed method, especially more than 6.6% Rank1 accuracy improvement is achieved on TinyFace. |
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言語 |
en |
| 出版者 |
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出版者 |
Elsevier |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
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出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/TIP.2023.3335830 |
| 収録物識別子 |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1057-7149 |
| 書誌情報 |
en : IEEE Transactions on Image Processing
巻 33,
p. 95-107,
発行日 2023-11-30
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TIP.pdf (10.4 MB)
