# Faceness-Net: Face Detection through Deep Facial Part Responses

arXiv: https://arxiv.org/abs/1701.08393 · HTML: https://mlsanity.com/paper/1701.08393/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Occlusion Handling in Generic Object Detection: A Review](https://mlsanity.com/paper/2101.08845/) (2021)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Now You See Me: Robust approach to Partial Occlusions](https://mlsanity.com/paper/2304.11779/) (2023)
- [Towards robustness under occlusion for face recognition](https://mlsanity.com/paper/2109.09083/) (2021)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform](https://mlsanity.com/paper/2208.11011/) (2022)
- [A Lightweight and Accurate Face Detection Algorithm Based on Retinaface](https://mlsanity.com/paper/2308.04340/) (2023)
- [SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings](https://mlsanity.com/paper/2402.14143/) (2024)

## Abstract (arXiv)

We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variations. Our method achieves promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW, and WIDER FACE.