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@inproceedings{WACVW2023,

title = {Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition},

author = {Klemen Grm and Berk Ozata and Vitomir Struc and Hazim K. Ekenel},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/01/Meet_in_the_middle.pdf

https://arxiv.org/abs/2211.15225

https://openaccess.thecvf.com/content/WACV2023W/RWS/papers/Grm_Meet-in-the-Middle_Multi-Scale_Upsampling_and_Matching_for_Cross-Resolution_Face_Recognition_WACVW_2023_paper.pdf



},

year  = {2023},

date = {2023-01-06},

booktitle = {WACV workshops},

pages = {120-129},

abstract = {In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.},

keywords = {deep learning, face, face recognition, multi-scale matching, smart surveillance, surveillance, surveillance technology},

pubstate = {published},

tppubtype = {inproceedings}

}