# EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection

arXiv: https://arxiv.org/abs/2306.02277 · HTML: https://mlsanity.com/paper/2306.02277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 4 Jun 2023.

## Builds on (most cited first, 30 of 33)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [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)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic](https://mlsanity.com/paper/2103.04039/) (2021)
- [PyramidBox: A Context-assisted Single Shot Face Detector](https://mlsanity.com/paper/1803.07737/) (2018)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [FaceBoxes: A CPU Real-time Face Detector with High Accuracy](https://mlsanity.com/paper/1708.05234/) (2017)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [SSH: Single Stage Headless Face Detector](https://mlsanity.com/paper/1708.03979/) (2017)
- [TinaFace: Strong but Simple Baseline for Face Detection](https://mlsanity.com/paper/2011.13183/) (2020)
- [Selective Refinement Network for High Performance Face Detection](https://mlsanity.com/paper/1809.02693/) (2018)
- [LFFD: A Light and Fast Face Detector for Edge Devices](https://mlsanity.com/paper/1904.10633/) (2019)
- [RefineFace: Refinement Neural Network for High Performance Face Detection](https://mlsanity.com/paper/1909.04376/) (2019)
- [EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse](https://mlsanity.com/paper/1906.06579/) (2019)
- [Supervised Transformer Network for Efficient Face Detection](https://mlsanity.com/paper/1607.05477/) (2016)
- [FA-RPN: Floating Region Proposals for Face Detection](https://mlsanity.com/paper/1812.05586/) (2018)
- [MogFace: Towards a Deeper Appreciation on Face Detection](https://mlsanity.com/paper/2103.11139/) (2021)
- [ASFD: Automatic and Scalable Face Detector](https://mlsanity.com/paper/2201.10781/) (2022)
- [Accurate Face Detection for High Performance](https://mlsanity.com/paper/1905.01585/) (2019)
- [Face Detection Using Improved Faster RCNN](https://mlsanity.com/paper/1802.02142/) (2018)
- [Robust and High Performance Face Detector](https://mlsanity.com/paper/1901.02350/) (2019)

## Built on by (most cited first, 1 of 1)

- [Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection](https://mlsanity.com/paper/2610.05865/) (2026)

## Abstract (arXiv)

In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts methods.