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

Kaihua Tang, Seiichiro Kamata, Xiaonan Hou, Shouhong Ding, Lizhuang Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationComputer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
PublisherSpringer Verlag
Pages389-403
Number of pages15
Volume10113 LNCS
ISBN (Print)9783319541860
DOIs
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10113 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tang, K., Kamata, S., Hou, X., Ding, S., & Ma, L. (2017). Eigen-aging reference coding for cross-age face verification and retrieval. In Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers (Vol. 10113 LNCS, pp. 389-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10113 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-54187-7_26