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

arXiv: https://arxiv.org/abs/2211.15225 · HTML: https://mlsanity.com/paper/2211.15225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [Learning Spatial Attention for Face Super-Resolution](https://mlsanity.com/paper/2012.01211/) (2020)
- [Super-Identity Convolutional Neural Network for Face Hallucination](https://mlsanity.com/paper/1811.02328/) (2018)
- [Face Hallucination via Split-Attention in Split-Attention Network](https://mlsanity.com/paper/2010.11575/) (2020)
- [PatchShuffle Regularization](https://mlsanity.com/paper/1707.07103/) (2017)
- [Face hallucination using cascaded super-resolution and identity priors](https://mlsanity.com/paper/1805.10938/) (2018)
- [Cross-Resolution Learning for Face Recognition](https://mlsanity.com/paper/1912.02851/) (2019)
- [FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization](https://mlsanity.com/paper/1911.11680/) (2019)
- [The Do's and Don'ts for CNN-based Face Verification](https://mlsanity.com/paper/1705.07426/) (2017)
- [Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition](https://mlsanity.com/paper/2108.01286/) (2021)
- [Exploring Factors for Improving Low Resolution Face Recognition](https://mlsanity.com/paper/1907.10104/) (2019)
- [Face Hallucination Revisited: An Exploratory Study on Dataset Bias](https://mlsanity.com/paper/1812.09010/) (2018)
- [NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition](https://mlsanity.com/paper/2103.03503/) (2021)

## Built on by (most cited first, 2 of 2)

- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [Analyzing the Effect of Combined Degradations on Face Recognition](https://mlsanity.com/paper/2406.02142/) (2024)

## Abstract (arXiv)

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.