# Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture

arXiv: https://arxiv.org/abs/1706.06247 · HTML: https://mlsanity.com/paper/1706.06247/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 17 of 17)

- [Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/2108.10290/) (2021)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)
- [ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition](https://mlsanity.com/paper/2407.14972/) (2024)
- [Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation](https://mlsanity.com/paper/2409.02555/) (2024)
- [Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline](https://mlsanity.com/paper/2302.05621/) (2023)
- [Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation](https://mlsanity.com/paper/2303.08665/) (2023)
- [Susceptibility to Image Resolution in Face Recognition and Trainings Strategies](https://mlsanity.com/paper/2107.03769/) (2021)
- [Boosting Unconstrained Face Recognition with Targeted Style Adversary](https://mlsanity.com/paper/2408.07642/) (2024)
- [DArFace: Deformation Aware Robustness for Low Quality Face Recognition](https://mlsanity.com/paper/2505.08423/) (2025)
- [Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator](https://mlsanity.com/paper/2111.14339/) (2021)
- [Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version)](https://mlsanity.com/paper/2306.17323/) (2023)
- [An Enhanced Low-Resolution Image Recognition Method for Traffic Environments](https://mlsanity.com/paper/2309.16390/) (2023)
- [Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation](https://mlsanity.com/paper/2409.02049/) (2024)
- [MSConv: Multiplicative and Subtractive Convolution for Face Recognition](https://mlsanity.com/paper/2503.06187/) (2025)
- [On What We Can Learn from Low-Resolution Data](https://mlsanity.com/paper/2605.12168/) (2026)

## Abstract (arXiv)

We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.