# Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

arXiv: https://arxiv.org/abs/2103.13851 · HTML: https://mlsanity.com/paper/2103.13851/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors](https://mlsanity.com/paper/1711.10703/) (2017)
- [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)
- [Studying Very Low Resolution Recognition Using Deep Networks](https://mlsanity.com/paper/1601.04153/) (2016)
- [On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques](https://mlsanity.com/paper/1805.11529/) (2018)
- [Face hallucination using cascaded super-resolution and identity priors](https://mlsanity.com/paper/1805.10938/) (2018)
- [Dual Directed Capsule Network for Very Low Resolution Image Recognition](https://mlsanity.com/paper/1908.10027/) (2019)
- [Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes](https://mlsanity.com/paper/1405.1207/) (2014)
- [SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination](https://mlsanity.com/paper/1807.08370/) (2018)
- [Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation](https://mlsanity.com/paper/2006.15710/) (2020)
- [Global versus Localized Generative Adversarial Nets](https://mlsanity.com/paper/1711.06020/) (2017)
- [Learning to Hallucinate Face Images via Component Generation and Enhancement](https://mlsanity.com/paper/1708.00223/) (2017)
- [Exploiting temporal and depth information for multi-frame face anti-spoofing](https://mlsanity.com/paper/1811.05118/) (2018)
- [Large-scale Bisample Learning on ID Versus Spot Face Recognition](https://mlsanity.com/paper/1806.03018/) (2018)
- [Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination](https://mlsanity.com/paper/1806.10726/) (2018)
- [Exploring Factors for Improving Low Resolution Face Recognition](https://mlsanity.com/paper/1907.10104/) (2019)
- [Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning](https://mlsanity.com/paper/1809.00665/) (2018)

## Built on by (most cited first, 6 of 6)

- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation](https://mlsanity.com/paper/2109.00699/) (2021)
- [Snow Mask Guided Adaptive Residual Network for Image Snow Removal](https://mlsanity.com/paper/2207.04754/) (2022)
- [W-Net: A Facial Feature-Guided Face Super-Resolution Network](https://mlsanity.com/paper/2406.00676/) (2024)
- [Leaning Compact and Representative Features for Cross-Modality Person Re-Identification](https://mlsanity.com/paper/2103.14210/) (2021)
- [Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods](https://mlsanity.com/paper/2410.00285/) (2024)

## Abstract (arXiv)

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches.