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

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

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.

Keywords

Face recognition Face verification Age invariant Discriminative metric learning Convolutional feature descriptors 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Saitama Institute of TechnologyFukaya SaitamaJapan
  2. 2.Mie UniversityTsu MieJapan
  3. 3.Yahoo Japan CorporationTokyoJapan

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