Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
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Facial landmark detection is an important but challenging task for real-world computer vision applications. This paper proposes an accurate and robust approach for facial landmark detection by combining data-driven and model-driven methods. Firstly, a fully convolutional network (FCN) is trained to generate response maps of all facial landmark points. Such a data-driven method can make full use of holistic information in a facial image for global estimation of facial landmarks. Secondly, the maximum points in the response maps are fitted with a pre-trained point distribution model (PDM) to generate initial facial landmark shape. Such a model-driven method can correct the location errors of outliers by considering shape prior information. Thirdly, a weighted version of Regularized Landmark Mean-Shift (RLMS) is proposed to fine-tune facial landmark shapes iteratively. The weighting strategy is based on the confidence of convolutional response maps so that FCN is integrated into the framework of Constrained Local Model (CLM). Such an Estimation-Correction-Tuning process perfectly combines the global robustness advantage of data-driven method (FCN), outlier correction advantage of model-driven method (PDM) and non-parametric optimization advantage of RLMS. The experimental results demonstrate that the proposed approach outperforms state-of-the-art solutions on the 300-W dataset. Our approach is well-suited for face images with large poses, exaggerated expression, and occlusions.
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