Eigen-aging reference coding for cross-age face verification and retrieval

Kaihua Tang, Sei Ichiro Kamata, Xiaonan Hou, Shouhong Ding, Lizhuang Ma

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Recent works have achieved near or over human performance in traditional face recognition under PIE (pose, illumination and expression) variation. However, few works focus on the cross-age face recognition task, which means identifying the faces from same person at different ages. Taking human-aging into consideration broadens the application area of face recognition. It comes at the cost of making existing algorithms hard to maintain effectiveness. This paper presents a new reference based approach to address cross-age problem, called Eigen-Aging Reference Coding (EARC). Different from other existing reference based methods, our reference traces eigen faces instead of specific individuals. The proposed reference has smaller size and contains more useful information. To the best of our knowledge, we achieve state-of-the-art performance and speed on CACD dataset, the largest public face dataset containing significant aging information.

本文言語English
ホスト出版物のタイトルComputer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
編集者Yoichi Sato, Shang-Hong Lai, Ko Nishino, Vincent Lepetit
出版社Springer Verlag
ページ389-403
ページ数15
ISBN(印刷版)9783319541860
DOI
出版ステータスPublished - 2017

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10113 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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