  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.
