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AdaptiveNet: Toward an Efficient Face Alignment Algorithm

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Biometric Recognition (CCBR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11818))

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Abstract

Face alignment, a challenging task in computer vision, has witnessed its tremendous improvement on the 300W benchmark. However, state-of-the-art algorithms are suffering from computational expense and therefore cannot apply in real-time. In this paper, we propose a time-efficient face alignment algorithm while maintain a sufficient algorithmic accuracy. Specifically, we adopt MobileNet-V2 as our backbone architecture to deal with easy samples, accompanied by a ResNet branch to handle hard examples. This combination leads to a low-latency and yet agreeable-performance design as our extensive experiment shows.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61573068 and 61871052.

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing, China

    Xiehe Huang & Weihong Deng

Authors
  1. Xiehe Huang
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  2. Weihong Deng
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Corresponding author

Correspondence to Weihong Deng .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  2. Chinese Academy of Sciences, Beijing, China

    Ran He

  3. Tsinghua University, Beijing, China

    Jianjiang Feng

  4. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  5. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Huang, X., Deng, W. (2019). AdaptiveNet: Toward an Efficient Face Alignment Algorithm. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-31456-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31455-2

  • Online ISBN: 978-3-030-31456-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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