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)


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.

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
出版ステータスPublished - 2017


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10113 LNCS

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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