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Chinese Conference on Pattern Recognition

CCPR 2016: Pattern Recognition pp 191–200Cite as

Pose-Invariant Face Recognition Based on a Flexible Camera Calibration

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

Abstract

In this paper, we present a flexible camera calibration for pose normalization to accomplish a pose-invariant face recognition. The accuracy of calibration can be easily influenced by errors of landmark detection or various shapes of different faces and expressions. By jointly using RANSAC and facial unique characters, we explore a flexible calibration method to achieve a more accurate camera calibration and pose normalization for face images. Our proposed method is able to eliminate noisy facial landmarks and retain the ones which best match the undeformable 3D face model. The experimental results show that our method improves the accuracy of pose-invariant face recognition, especially for the faces with unsatisfied landmark detection, variant shapes, and exaggerated expressions.

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Acknowledgement

This work was supported by the Project from National Science Foundation for Youths of China (No. 61502444), National Natural Science Foundation of China (No. 61472386), Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA06040103). Two Titan X GPUs applied for this research were donated by the NVIDIA Corporation.

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Authors and Affiliations

  1. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

    Xiaohu Shao, Cheng Cheng, Yanfei Liu & Xiangdong Zhou

Authors
  1. Xiaohu Shao
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  2. Cheng Cheng
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  3. Yanfei Liu
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  4. Xiangdong Zhou
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Corresponding author

Correspondence to Cheng Cheng .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Shao, X., Cheng, C., Liu, Y., Zhou, X. (2016). Pose-Invariant Face Recognition Based on a Flexible Camera Calibration. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_16

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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