# Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

arXiv: https://arxiv.org/abs/2303.08665 · HTML: https://mlsanity.com/paper/2303.08665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Low-Resolution Face Recognition](https://mlsanity.com/paper/1811.08965/) (2018)
- [Wavelet Integrated CNNs for Noise-Robust Image Classification](https://mlsanity.com/paper/2005.03337/) (2020)
- [Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation](https://mlsanity.com/paper/1811.09998/) (2018)
- [Super-Identity Convolutional Neural Network for Face Hallucination](https://mlsanity.com/paper/1811.02328/) (2018)
- [Cross-Resolution Learning for Face Recognition](https://mlsanity.com/paper/1912.02851/) (2019)
- [Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition](https://mlsanity.com/paper/2209.14498/) (2022)
- [FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization](https://mlsanity.com/paper/1911.11680/) (2019)
- [Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations](https://mlsanity.com/paper/1710.01494/) (2017)
- [Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture](https://mlsanity.com/paper/1706.06247/) (2017)
- [SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination](https://mlsanity.com/paper/1807.08370/) (2018)
- [Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation](https://mlsanity.com/paper/1905.10777/) (2019)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)

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

- [Analyzing the Effect of Combined Degradations on Face Recognition](https://mlsanity.com/paper/2406.02142/) (2024)
- [Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition](https://mlsanity.com/paper/2409.06371/) (2024)

## Abstract (arXiv)

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.