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International Conference on Pattern Recognition and Artificial Intelligence

ICPRAI 2020: Pattern Recognition and Artificial Intelligence pp 513–524Cite as

Rotation-Invariant Face Detection with Multi-task Progressive Calibration Networks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

Abstract

Rotation-invariant face detection (RIFD) aims to detect faces with arbitrary rotation-in-plane (RIP) on both images and video sequences, which is challenging because of large appearance variations of rotated faces under unconstrained scenarios. The problem becomes more formidable when the speed and memory efficiency of the detectors are taken into account. To solve this problem, we propose a Multi-Task Progressive Calibration Networks (MTPCN), which not only enjoy the natural advantage of explicit geometric structure representation, but also retain important cue to guide the feature learning for precise calibration. More concretely, our framework leverages a cascaded architecture with three stages of carefully designed convolutional neural networks to predict face and landmark location with gradually decreasing RIP ranges. In addition, we propose a novel loss by further integrating geometric information into penalization, which is much more reasonable than simply measuring the differences of training samples equally. MTPCN achieves significant performance improvement on the multi-oriented FDDB dataset and the rotation WIDER face dataset. Extensive experiments are performed, and demonstrate the effectiveness of our method for each of three tasks.

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Acknowledgment

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K201900601), by the National Natural Science Foundation of Chongqing (Grant No. cstc2019jcyj-msxmX0461), and by the Chongqing Overseas Scholars Innovation Program (Grant No. E020H2018013).

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Li-Fang Zhou & Yu Gu

  2. College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Li-Fang Zhou, Jie Liu & Tian-Yu Xu

  3. Northeastern University, Boston, MA, 02115, USA

    Patrick S. P. Wang

  4. Petrochina Chongqing Marketing Company, Chongqing, 400065, China

    Fa-Yuan Liu

Authors
  1. Li-Fang Zhou
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  2. Yu Gu
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  3. Patrick S. P. Wang
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  4. Fa-Yuan Liu
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  5. Jie Liu
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  6. Tian-Yu Xu
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Corresponding author

Correspondence to Li-Fang Zhou .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Cite this paper

Zhou, LF., Gu, Y., Wang, P.S.P., Liu, FY., Liu, J., Xu, TY. (2020). Rotation-Invariant Face Detection with Multi-task Progressive Calibration Networks. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_44

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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