Towards Understanding Cross Resolution Feature Matching for Surveillance Face Recognition-Reference-Cited by-同舟云学术

Towards Understanding Cross Resolution Feature Matching for Surveillance Face Recognition

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM International Conference on Multimedia
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Author:

Kuo Chiawei¹,Tsai Yi-Ting¹,Shuai Hong-Han¹,Yeh Yi-ren²,Huang Ching-Chun¹

Affiliation:

1. National Yang Ming Chiao Tung University, Hsinchu City, Taiwan Roc

2. National Kaohsiung Normal University, Kaohsiung City, Taiwan Roc

Funder

the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and Ministry of Education (MOE) Taiwan

E.SUN COMMERCIAL BANK LTD

the Ministry of Science and Technology (MOST) Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503161.3548402

Reference45 articles.

1. Exploring Factors for Improving Low Resolution Face Recognition

2. Adrian Bulat , Jing Yang , and Georgios Tzimiropoulos . 2018 . To learn image superresolution, use a gan to learn how to do image degradation first . In Proceedings of the European conference on computer vision (ECCV). 185--200 . Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos. 2018. To learn image superresolution, use a gan to learn how to do image degradation first. In Proceedings of the European conference on computer vision (ECCV). 185--200.

3. VGGFace2: A Dataset for Recognising Faces across Pose and Age

4. Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition

5. Learning Continuous Image Representation with Local Implicit Image Function

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