# Supervised Transformer Network for Efficient Face Detection

arXiv: https://arxiv.org/abs/1607.05477 · HTML: https://mlsanity.com/paper/1607.05477/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2016.

## Built on by (most cited first, 14 of 14)

- [HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details](https://mlsanity.com/paper/2303.11225/) (2023)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)
- [VirtualCube: An Immersive 3D Video Communication System](https://mlsanity.com/paper/2112.06730/) (2021)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Affective Video Content Analysis: Decade Review and New Perspectives](https://mlsanity.com/paper/2310.17212/) (2023)
- [Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2112.14382/) (2021)
- [EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection](https://mlsanity.com/paper/2302.11816/) (2023)
- [High-Quality Facial Albedo Generation for 3D Face Reconstruction from a Single Image using a Coarse-to-Fine Approach](https://mlsanity.com/paper/2506.13233/) (2025)
- [You Better Look Twice: a new perspective for designing accurate detectors with reduced computations](https://mlsanity.com/paper/2107.10050/) (2021)
- [EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection](https://mlsanity.com/paper/2306.02277/) (2023)
- [Impact of Face Alignment on Face Image Quality](https://mlsanity.com/paper/2412.11779/) (2024)
- [From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion](https://mlsanity.com/paper/2204.03842/) (2022)
- [Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification](https://mlsanity.com/paper/2205.07203/) (2022)
- [Exploring Disparity-Accuracy Trade-offs in Face Recognition Systems: The Role of Datasets, Architectures, and Loss Functions](https://mlsanity.com/paper/2503.14138/) (2025)

## Abstract (arXiv)

Large pose variations remain to be a challenge that confronts real-word face detection. We propose a new cascaded Convolutional Neural Network, dubbed the name Supervised Transformer Network, to address this challenge. The first stage is a multi-task Region Proposal Network (RPN), which simultaneously predicts candidate face regions along with associated facial landmarks. The candidate regions are then warped by mapping the detected facial landmarks to their canonical positions to better normalize the face patterns. The second stage, which is a RCNN, then verifies if the warped candidate regions are valid faces or not. We conduct end-to-end learning of the cascaded network, including optimizing the canonical positions of the facial landmarks. This supervised learning of the transformations automatically selects the best scale to differentiate face/non-face patterns. By combining feature maps from both stages of the network, we achieve state-of-the-art detection accuracies on several public benchmarks. For real-time performance, we run the cascaded network only on regions of interests produced from a boosting cascade face detector. Our detector runs at 30 FPS on a single CPU core for a VGA-resolution image.