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Spatial alignment network for facial landmark localization

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Abstract

Facial Landmark Localization (FLL) on unconstrained images still remains challenging as they poses complex variation in face spatial structure and appearance. To address this problem, we propose a Spatial Alignment Network (SAN), which consist of two modules, like the transformation sub-network and the estimation sub-network. In the first module, we propose two methods to achieving spatial transformation, one is the handcrafted method which can ensure model stability and the other is the learning-based method which is efficient and flexible. In the second module, we add an attention layer in the deep CNN to enhance the importance of discriminative features and obtain more accurate results. Through extensive experiments, our model achieves good performance on several public challenging datasets.

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Acknowledgements

This work is supported by National Science Foundation of China Grant #61672088 and #61790575, Fundamental Research Funds for the Central Universities #2018JBZ002. The corresponding author is Yidong Li.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Huifang Li & Yidong Li

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Junliang Xing

  3. Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Hairong Dong

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  1. Huifang Li
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  2. Yidong Li
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  3. Junliang Xing
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  4. Hairong Dong
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Correspondence to Yidong Li.

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Li, H., Li, Y., Xing, J. et al. Spatial alignment network for facial landmark localization. World Wide Web 22, 1481–1498 (2019). https://doi.org/10.1007/s11280-018-0615-9

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