# Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild

arXiv: https://arxiv.org/abs/1705.02402 · HTML: https://mlsanity.com/paper/1705.02402/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2017.

## Built on by (most cited first, 7 of 7)

- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement](https://mlsanity.com/paper/2103.07017/) (2021)
- [Temporal Aggregation for Adaptive RGBT Tracking](https://mlsanity.com/paper/2201.08949/) (2022)
- [A Survey for Deep RGBT Tracking](https://mlsanity.com/paper/2201.09296/) (2022)
- [Collaborative Representation for SPD Matrices with Application to Image-Set Classification](https://mlsanity.com/paper/2201.08962/) (2022)
- [Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training](https://mlsanity.com/paper/2101.06663/) (2021)
- [Facial Landmark Detection Evaluation on MOBIO Database](https://mlsanity.com/paper/2307.03329/) (2023)

## Abstract (arXiv)

We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary detectors. We aggregate the detected face bounding boxes of each input image to reduce false positives and improve face detection accuracy. A cascaded shape regressor, trained using faces with a variety of pose variations, is then employed for pose estimation and image pre-processing. Last, we train the final cascaded shape regressor for fine-grained landmark localisation, using a large number of training samples with limited pose variations. The experimental results obtained on the 300W and Menpo benchmarks demonstrate the superiority of our framework over state-of-the-art methods.