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

TL;DR

This paper introduces a robust facial landmark detection method that combines deep learning, shape modeling, and iterative refinement to improve accuracy under challenging conditions.

Contribution

It proposes a novel hybrid approach integrating FCN, PDM, and RLMS for enhanced robustness and accuracy in facial landmark detection.

Findings

Achieves state-of-the-art performance on multiple challenging datasets.

Effectively handles faces with expressions, poses, and occlusions.

Demonstrates robustness and high accuracy in real-world scenarios.

Abstract

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.