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Intensity-Depth Face Alignment Using Cascade Shape Regression

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492))

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Abstract

With quick development of Kinect, depth image has become an important channel for assisting the color/infrared image in diverse computer vision tasks. Kinect can provide depth image as well as color and infrared images, which are suitable for multi-model vision tasks. This paper presents a framework for intensity-depth face alignment based on cascade shape regression. Information from intensity and depth images is combined during feature selection in cascade shape regression. Experimental results show that this combination improves face alignment accuracy notably.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61272248), the National Basic Research Program of China (Grant No. 2013CB329401), and the Science and Technology Commission of Shanghai Municipality (Grant No. 13511500200).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Center for Brain-like Computing and Machine Intelligence, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Yang Cao & Bao-Liang Lu

  2. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Bao-Liang Lu

Authors
  1. Yang Cao
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  2. Bao-Liang Lu
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Corresponding author

Correspondence to Bao-Liang Lu .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Cao, Y., Lu, BL. (2015). Intensity-Depth Face Alignment Using Cascade Shape Regression. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-26561-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26560-5

  • Online ISBN: 978-3-319-26561-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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