TY - GEN
T1 - Eigen-Aging Reference Coding for Cross-Age Face Verification and Retrieval
AU - Tang, Kaihua
AU - Kamata, Sei Ichiro
AU - Hou, Xiaonan
AU - Ding, Shouhong
AU - Ma, Lizhuang
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105036870193
U2 - 10.1007/978-3-319-54187-7 26
DO - 10.1007/978-3-319-54187-7 26
M3 - 会议稿件
AN - SCOPUS:105036870193
SN - 9783319541860
T3 - Lecture Notes in Computer Science
SP - 389
EP - 403
BT - Computer Vision – ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers, Part 3
A2 - Lai, Shang-Hong
A2 - Nishino, Ko
A2 - Lepetit, Vincent
A2 - Sato, Yoichi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -