# CenterFace: Joint Face Detection and Alignment Using Face as Point

arXiv: https://arxiv.org/abs/1911.03599 · HTML: https://mlsanity.com/paper/1911.03599/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 13 of 13)

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- [PanoWorld: Geometry-Consistent Panoramic Video World Modeling](https://mlsanity.com/paper/2605.15391/) (2026)
- [OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments](https://mlsanity.com/paper/2603.02390/) (2026)
- [Impact of Face Alignment on Face Image Quality](https://mlsanity.com/paper/2412.11779/) (2024)
- [To show or not to show: Redacting sensitive text from videos of electronic displays](https://mlsanity.com/paper/2208.10270/) (2022)
- [A Comparative Study of Face Detection Algorithms for Masked Face Detection](https://mlsanity.com/paper/2305.11077/) (2023)
- [Segmentation Dataset for Reinforced Concrete Construction](https://mlsanity.com/paper/2407.09372/) (2024)
- [Investigating Adversarial Robustness against Preprocessing used in Blackbox Face Recognition](https://mlsanity.com/paper/2510.17169/) (2025)
- [NextMe-800: Anticipating Personal Behavior from Months of Egocentric Video](https://mlsanity.com/paper/2610.01461/) (2026)

## Abstract (arXiv)

Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power. This paper proposes a one-stage method named CenterFace to simultaneously predict facial box and landmark location with real-time speed and high accuracy. The proposed method also belongs to the anchor free category. This is achieved by: (a) learning face existing possibility by the semantic maps, (b) learning bounding box, offsets and five landmarks for each position that potentially contains a face. Specifically, the method can run in real-time on a single CPU core and 200 FPS using NVIDIA 2080TI for VGA-resolution images, and can simultaneously achieve superior accuracy (WIDER FACE Val/Test-Easy: 0.935/0.932, Medium: 0.924/0.921, Hard: 0.875/0.873 and FDDB discontinuous: 0.980, continuous: 0.732). A demo of CenterFace can be available at https://github.com/Star-Clouds/CenterFace.