# Inverted Non-maximum Suppression for more Accurate and Neater Face Detection

arXiv: https://arxiv.org/abs/2305.10593 · HTML: https://mlsanity.com/paper/2305.10593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [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)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [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)
- [Adaptive NMS: Refining Pedestrian Detection in a Crowd](https://mlsanity.com/paper/1904.03629/) (2019)
- [PyramidBox: A Context-assisted Single Shot Face Detector](https://mlsanity.com/paper/1803.07737/) (2018)
- [EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse](https://mlsanity.com/paper/1906.06579/) (2019)
- [A convnet for non-maximum suppression](https://mlsanity.com/paper/1511.06437/) (2015)

## Abstract (arXiv)

CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding-boxes for one face. NMS is used to filter out inaccurate candidate boxes to get the most accurate box. The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score. However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster. In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes. To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods. Our method performs well on low-quality and tiny face samples. Experiments demonstrate that our method is effective as a post-processor for different face detection methods.