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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2019: Trends and Applications in Knowledge Discovery and Data Mining pp 214–222Cite as

Label Distribution Learning Based Age-Invariant Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11607))

Abstract

Face recognition is an important application of computer vision. Al-though the accuracy of face recognition is high, face recognition and retrieval across age is still challenging. Faces across age can be very different caused by the aging process over time. The problem is that the images are not too similar, but with the same label. To reduce the intraclass discrepancy, in this paper we pro-pose a new method called Label Distribution learning for the end-to-end neural network to learn more discriminative features. Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and LFW) have shown the effectiveness of the proposed approach.

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Author information

Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Hai Huang, Senlin Cheng, Zhong Hong & Liutong Xu

Authors
  1. Hai Huang
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  2. Senlin Cheng
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  3. Zhong Hong
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  4. Liutong Xu
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Corresponding authors

Correspondence to Hai Huang or Senlin Cheng .

Editor information

Editors and Affiliations

  1. University of Macau, Macao, China

    Leong Hou U.

  2. Singapore Management University, Singapore, Singapore

    Hady W. Lauw

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Huang, H., Cheng, S., Hong, Z., Xu, L. (2019). Label Distribution Learning Based Age-Invariant Face Recognition. In: U., L., Lauw, H. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11607. Springer, Cham. https://doi.org/10.1007/978-3-030-26142-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-26142-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26141-2

  • Online ISBN: 978-3-030-26142-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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