Structure-Coherent Deep Feature Learning for Robust Face Alignment

In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021, Vol.30, p.5313-5326
Main Authors: Lin, Chunze, Zhu, Beier, Wang, Quan, Liao, Renjie, Qian, Chen, Lu, Jiwen, Zhou, Jie
Format: Article
Language:English
Subjects:
Alignment
Coherence
Computational modeling
Convolution
Deep learning
Face alignment
Face recognition
Faces
facial landmarks
Failure rates
graph convolution
Heating systems
Landmarks
Learning
Nodes
Occlusion
Robustness
Training
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Summary:In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. The model and code are publicly available at https://github.com/BeierZhu/Sturcture-Coherency-Face-Alignment
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3082319