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Discriminative Metric Learning with Convolutional Feature Descriptors for Age-Invariant Face Recognition and Verification

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Frontiers of Computer Vision (IW-FCV 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1212))

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Abstract

Aging includes internal and external factors that cause variation in appearance of face and, consequently, it is a difficult problem to handle in person identification and verification using face images. In this paper, we propose a method for face recognition and verification that is robust against variation of facial appearance caused by aging. Our proposed method uses discriminative metric learning over convolutional feature descriptors extracted from frontal face images. The results of an experiments for performance evaluation on the FG-Net and CACD face aging datasets empirically clarify that the proposed method is effective for improving the performance of person identification and verification in the scenario where input face images contain appearance variation due to aging.

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Notes

  1. 1.

    This separation was also used in [17].

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Authors and Affiliations

  1. Saitama Institute of Technology, Fukaya Saitama, Japan

    Wataru Ohyama

  2. Mie University, Tsu Mie, Japan

    Yuta Somada, Nobu C. Shirai & Tetsushi Wakabayashi

  3. Yahoo Japan Corporation, Tokyo, Japan

    Yuta Somada

Authors
  1. Wataru Ohyama
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  2. Yuta Somada
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  3. Nobu C. Shirai
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  4. Tetsushi Wakabayashi
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Corresponding author

Correspondence to Wataru Ohyama .

Editor information

Editors and Affiliations

  1. Saitama Institute of Technology, Saitama, Japan

    Wataru Ohyama

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Soon Ki Jung

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Ohyama, W., Somada, Y., Shirai, N.C., Wakabayashi, T. (2020). Discriminative Metric Learning with Convolutional Feature Descriptors for Age-Invariant Face Recognition and Verification. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_7

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  • DOI: https://doi.org/10.1007/978-981-15-4818-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4817-8

  • Online ISBN: 978-981-15-4818-5

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