Title: Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection
Authors: Zhang, HW; Li, Q; Sun, ZN; Liu, YF
Author Full Names: Zhang, Hongwen; Li, Qi; Sun, Zhenan; Liu, Yunfan
Source: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 13 (10):2409-2422; 10.1109/TIFS.2018.2800901 OCT 2018
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. First, 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 the data-driven method (FCN), outlier correction capability of the 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 data sets, 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.
IDS Number: GF3ZB
Unique ID: WOS:000431895700001