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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 319–333Cite as

Face Detection by Aggregating Visible Components

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

Abstract

Pose variations and occlusions are two major challenges for unconstrained face detection. Many approaches have been proposed to handle pose variations and occlusions in face detection, however, few of them addresses the two challenges in a model explicitly and simultaneously. In this paper, we propose a novel face detection method called Aggregating Visible Components (AVC), which addresses pose variations and occlusions simultaneously in a single framework with low complexity. The main contributions of this paper are: (1) By aggregating visible components which have inherent advantages in occasions of occlusions, the proposed method achieves state-of-the-art performance using only hand-crafted feature; (2) Mapped from meanshape through component-invariant mapping, the proposed component detector is more robust to pose-variations (3) A local to global aggregation strategy that involves region competition helps alleviate false alarms while enhancing localization accuracy.

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Notes

  1. 1.

    Blur or low resolution is a challenging problem mainly in surveillance. Though many blur face images exist in current benchmark databases (e.g. FDDB [1]), they are intentionally made out of focus in background while the main focus is the center figures in news photography.

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Acknowledgement

This work was supported by the National Key Research and Development Plan (Grant No. 2016YFC0801002), the Chinese National Natural Science Foundation Projects #61672521, #61473291, #61572501, #61502491, #61572536, NVIDIA GPU donation program and AuthenMetric R&D Funds.

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Authors and Affiliations

  1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China

    Jiali Duan

  2. Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shengcai Liao & Stan Z. Li

  3. School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China

    Xiaoyuan Guo

Authors
  1. Jiali Duan
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  2. Shengcai Liao
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  3. Xiaoyuan Guo
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  4. Stan Z. Li
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Corresponding author

Correspondence to Jiali Duan .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Duan, J., Liao, S., Guo, X., Li, S.Z. (2017). Face Detection by Aggregating Visible Components. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_24

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  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

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