# Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection

arXiv: https://arxiv.org/abs/1611.10152 · HTML: https://mlsanity.com/paper/1611.10152/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

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

- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [Multi-task head pose estimation in-the-wild](https://mlsanity.com/paper/2202.02299/) (2022)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- [OTB-morph: One-Time Biometrics via Morphing](https://mlsanity.com/paper/2302.09053/) (2023)
- [Dense Representative Tooth Landmark/axis Detection Network on 3D Model](https://mlsanity.com/paper/2111.04212/) (2021)
- [Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset](https://mlsanity.com/paper/2104.12345/) (2021)
- [LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection](https://mlsanity.com/paper/2507.21756/) (2025)
- [Facial Landmark Detection Evaluation on MOBIO Database](https://mlsanity.com/paper/2307.03329/) (2023)

## Abstract (arXiv)

Facial landmark detection is an important yet challenging task for real-world computer vision applications. This paper proposes an effective and robust approach for facial landmark detection by combining data- and model-driven methods. Firstly, a Fully Convolutional Network (FCN) is trained to compute response maps of all facial landmark points. Such a data-driven method could make full use of holistic information in a facial image for global estimation of facial landmarks. After that, the maximum points in the response maps are fitted with a pre-trained Point Distribution Model (PDM) to generate the initial facial shape. This model-driven method is able to correct the inaccurate locations of outliers by considering the shape prior information. Finally, a weighted version of Regularized Landmark Mean-Shift (RLMS) is employed to fine-tune the facial shape iteratively. This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS. Results of extensive experiments demonstrate that our approach achieves state-of-the-art performances on challenging datasets including 300W, AFLW, AFW and COFW. The proposed method is able to produce satisfying detection results on face images with exaggerated expressions, large head poses, and partial occlusions.