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

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

doi:10.1007/978-3-319-54187-7_26

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

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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
Editors	Yoichi Sato, Shang-Hong Lai, Ko Nishino, Vincent Lepetit
Publisher	Springer Verlag
Pages	389-403
Number of pages	15
ISBN (Print)	9783319541860
DOIs	https://doi.org/10.1007/978-3-319-54187-7_26
State	Published - 2017
Externally published	Yes

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)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-319-54187-7_26

Cite this

Tang, K., Kamata, S. I., Hou, X., Ding, S., & Ma, L. (2017). Eigen-aging reference coding for cross-age face verification and retrieval. In Y. Sato, S.-H. Lai, K. Nishino, & V. Lepetit (Eds.), Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers (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

Tang, Kaihua ; Kamata, Sei Ichiro ; Hou, Xiaonan et al. / Eigen-aging reference coding for cross-age face verification and retrieval. Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. editor / Yoichi Sato ; Shang-Hong Lai ; Ko Nishino ; Vincent Lepetit. Springer Verlag, 2017. pp. 389-403 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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

Eigen-aging reference coding for cross-age face verification and retrieval. / Tang, Kaihua; Kamata, Sei Ichiro; Hou, Xiaonan et al.
Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. ed. / Yoichi Sato; Shang-Hong Lai; Ko Nishino; Vincent Lepetit. 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 › peer-review

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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.

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BT - Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers

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Tang K, Kamata SI, Hou X, Ding S, Ma L. Eigen-aging reference coding for cross-age face verification and retrieval. In Sato Y, Lai SH, Nishino K, Lepetit V, editors, Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Springer Verlag. 2017. p. 389-403. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-54187-7_26

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

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