# Face Detection: Present State and Research Directions

arXiv: https://arxiv.org/abs/2402.03796 · HTML: https://mlsanity.com/paper/2402.03796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Face Detection with the Faster R-CNN](https://mlsanity.com/paper/1606.03473/) (2016)

## Abstract (arXiv)

The majority of computer vision applications that handle images featuring humans use face detection as a core component. Face detection still has issues, despite much research on the topic. Face detection's accuracy and speed might yet be increased. This review paper shows the progress made in this area as well as the substantial issues that still need to be tackled. The paper provides research directions that can be taken up as research projects in the field of face detection.