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 language | English |
|---|---|
| Title of host publication | Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers |
| Publisher | Springer Verlag |
| Pages | 389-403 |
| Number of pages | 15 |
| Volume | 10113 LNCS |
| ISBN (Print) | 9783319541860 |
| DOIs | |
| Publication status | Published - 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10113 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Eigen-aging reference coding for cross-age face verification and retrieval. / Tang, Kaihua; Kamata, Seiichiro; Hou, Xiaonan; Ding, Shouhong; Ma, Lizhuang.
Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Vol. 10113 LNCS Springer Verlag, 2017. p. 389-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10113 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Eigen-aging reference coding for cross-age face verification and retrieval
AU - Tang, Kaihua
AU - Kamata, Seiichiro
AU - Hou, Xiaonan
AU - Ding, Shouhong
AU - Ma, Lizhuang
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 - http://www.scopus.com/inward/record.url?scp=85016073072&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016073072&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54187-7_26
DO - 10.1007/978-3-319-54187-7_26
M3 - Conference contribution
AN - SCOPUS:85016073072
SN - 9783319541860
VL - 10113 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 389
EP - 403
BT - Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
PB - Springer Verlag
ER -