Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition

• URL: http://arxiv.org/abs/2211.15225v2
• Date: Tue, 29 Nov 2022 09:28:14 GMT
• Title: Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
• Authors: Klemen Grm, Berk Kemal \"Ozata, Vitomir \v{S}truc, Haz{\i}m Kemal Ekenel
• Abstract summary: We propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from surveillance footage. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images.
• Score: 8.330506641637793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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