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Facial Age Estimation Based on Structured Low-rank Representation

Published: 13 October 2015 Publication History

Abstract

This paper presents an algorithm based on structured, low- rank representation for facial age estimation. The proposed method learns the discriminative feature representation of images with the constraint of the classwise block-diagonal structure to promote discrimination of representations for robust recognition. A block-sparse regularizer is introduced to exploit the similarity and structure information of class. Based on the new representation, we estimate the accurate age using a regression function. By subtly introducing the structured, low-rank representation, we achieve good age estimation performance. Experimental results on three well-known aging faces datasets have demonstrated that the proposed method is superior to the conventional approaches.

References

[1]
Fg-net aging dataset. http://sting.cycollege.ac.cy/alnantis/fgnetaging.html, 2002.
[2]
K. Chang, C. Chen, and Y. Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In IEEE Conf. CVPR, volume 42, pages 585--592, 2011.
[3]
T. F. Edwards GJ and C. J. Taylor. Active appearance models. IEEE Trans. on PAMI, 23(6):681--685, 2001.
[4]
X. Geng, C.Yin, and Z. Zhou. Facial age estimation by learning from label distributions. IEEE Trans. on PAMI, 35(10):1--12, 2014.
[5]
X. Geng, Z. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Trans. on PAMI, 29(12):2234--2240, 2007.
[6]
G. Guo, Y. Fu, C. Dyer, and T. Huang. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Processing, 17(7):1178--1188, 2008.
[7]
A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Trans. on SMC, 34:621--628, 2004.
[8]
Y. Li, J. Liu, Z. Li, Y. Zhang, H. Lu, and S. Ma. Learning low-rank representations with classwise block-diagonal structure for robust face recognition. In AAAI 2014, volume 9, pages 2810--2816, 2014.
[9]
Y. Li, J. Liu, H. Lu, and S. Ma. Learning robust face representation with classwise block-diagonal structure. IEEE Trans. on IFS, 9(12):2051--2062, 2014.
[10]
Z. Lin, R. Liu, and Z. Su. Linearized alternating direction method with adaptive penality for low rank representation. In NIPS, pages 612--620, 2011.
[11]
G. Liu, Z. Liu, S. Yan, and J. Sun. Robust recovery of subspace structures by low-rank representation. IEEE Trans. on PAMI, 35(1):171--184, 2013.
[12]
B. Ni, Z. Song, and S. Yan. Web image mining towards universal age estimator. In ACM MM, pages 85--94, 2009.
[13]
Y. Pang, K. Zhang, Y. Yuan, and K. Wang. Distributed object detection with linear svms. IEEE Trans. on Cybernetics, 44(11):2122--2133, 2014.
[14]
K. Ricanek and T. Tesafaye. Morph: A longitudinal image dataset of normal adult age-progression. In IEEE CS, pages 341--345, 2006.
[15]
Y. Zhang, Z. Jiang, and D. L.S. Learning structured low-rank representations for image classification. In IEEE Conf. CVPR, volume 74, pages 1--21, 2013.
[16]
L. Zhuang, H. Gao, Z. Lin, Y. Ma, X. Zhang, and N. Yu. Non-negative low rank and sparse graph for semi-supervised learning. IEEE Conf. CVPR, 157(10):2328--2335, 2012

Cited By

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  • (2016)Facial Age Estimation Using Robust Label DistributionProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967186(77-81)Online publication date: 1-Oct-2016

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  1. Facial Age Estimation Based on Structured Low-rank Representation

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    cover image ACM Conferences
    MM '15: Proceedings of the 23rd ACM international conference on Multimedia
    October 2015
    1402 pages
    ISBN:9781450334594
    DOI:10.1145/2733373
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    Publication History

    Published: 13 October 2015

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    Author Tags

    1. block-diagonal
    2. classification and regression
    3. facial age estimation
    4. low-rank representation

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    MM '15
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    MM '15: ACM Multimedia Conference
    October 26 - 30, 2015
    Brisbane, Australia

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    MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    • (2016)Facial Age Estimation Using Robust Label DistributionProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967186(77-81)Online publication date: 1-Oct-2016

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