# FDDB-360: Face Detection in 360-degree Fisheye Images

arXiv: https://arxiv.org/abs/1902.02777 · HTML: https://mlsanity.com/paper/1902.02777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://mlsanity.com/paper/2203.08399/) (2022)
- [Sector Patch Embedding: An Embedding Module Conforming to The Distortion Pattern of Fisheye Image](https://mlsanity.com/paper/2303.14645/) (2023)
- [Location-guided Head Pose Estimation for Fisheye Image](https://mlsanity.com/paper/2402.18320/) (2024)

## Abstract (arXiv)

360-degree cameras offer the possibility to cover a large area, for example an entire room, without using multiple distributed vision sensors. However, geometric distortions introduced by their lenses make computer vision problems more challenging. In this paper we address face detection in 360-degree fisheye images. We show how a face detector trained on regular images can be re-trained for this purpose, and we also provide a 360-degree fisheye-like version of the popular FDDB face detection dataset, which we call FDDB-360.