Learning a multi-center convolutional network for unconstrained face alignment

Zhiwen Shao, Hengliang Zhu, Yangyang Hao, Min Wang, Lizhuang Ma

Abstract: In this paper, we propose a novel multi-center convolutional neural network for unconstrained face alignment. To utilize structural correlations among different facial landmarks, we determine several clusters based on their spatial position. We pre-train our network to learn generic feature representations. We further fine-tune the pre-trained model to emphasize on locating a certain cluster of landmarks respectively. Fine-tuning contributes to searching an optimal solution smoothly without deviating from the pre-trained model excessively. We obtain an excellent solution by combining multiple fine-tuned models. Extensive experiments demonstrate that our method possesses superior capability of handling extreme occlusions and complex variations of pose, expression, illumination. The code for our method is available at https://github.com/ZhiwenShao/MCNet.