Cross-resolution learning for Face Recognition

作者：

Highlights：

• Deep learning models performance drops in cross-resolution Face Recognition scenario.

• Real world applications require resolution-robust models.

• The proposed strategy enables models to extract resolution robust deep features.

• Resolution-robust models improve upon state-of-the-art in cross-resolution scenarios.

• Low-resolution performances increase while improving cross-resolution.

摘要

•Deep learning models performance drops in cross-resolution Face Recognition scenario.•Real world applications require resolution-robust models.•The proposed strategy enables models to extract resolution robust deep features.•Resolution-robust models improve upon state-of-the-art in cross-resolution scenarios.•Low-resolution performances increase while improving cross-resolution.

论文关键词：Deep learning,Low resolution Face Recognition,Cross resolution Face Recognition

论文评审过程：Received 20 August 2019, Revised 28 April 2020, Accepted 3 May 2020, Available online 13 May 2020, Version of Record 26 May 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.103927