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Reconstruction Network for single-face detection and landmark localization

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Abstract

This paper introduces Reconstruction Network to reconstruct the regions of interest of one or more objects within an optical image without time-consuming image segmentation or key-point descriptors calculation. We evaluate Reconstruction Network using face detection and facial landmark localization. Experiments show that new algorithm learns the structure of face and facial landmarks automatically and obtains state-of-the-art performance for face detection (almost 50% higher detection rates than widely used method) and facial land-mark localization (0.03 lower mean error of key point location than two recently published methods) while requiring only a fraction of the computing resources.

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Acknowledgements

This work was supported by Volkswagen funding. We thank Bing Liu’s generous help from Carnegie Mellon University in improving language quality of our paper. We are appreciated to the National Natural Science Foundation of China (41601451), the International Partnership Program of the Chinese Academy of Sciences (131C11KYSB20160061), and the International Partnership Program of the Chinese Academy of Sciences (131551KYSB20160002).

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Author notes

  1. Bo Yu and Ian Lane have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing, 100094, China

    Bo Yu & Fang Chen

  2. Carnegie Mellon University, NASA Research Park #23, Moffett Field, CA, 94043, USA

    Bo Yu & Ian Lane

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fang Chen

  4. Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Sanya, 572029, China

    Fang Chen

Authors
  1. Bo Yu
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  2. Ian Lane
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  3. Fang Chen
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Correspondence to Fang Chen.

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Yu, B., Lane, I. & Chen, F. Reconstruction Network for single-face detection and landmark localization. Opt Quant Electron 49, 282 (2017). https://doi.org/10.1007/s11082-017-1118-0

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  • DOI: https://doi.org/10.1007/s11082-017-1118-0

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