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Cited by 101

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A Study of Face Recognition as People Age

Ling, Haibin, Soatto, Stefano, Ramanathan, Narayanan, Jacobs, David W.·2007·2007 IEEE 11th International Conference on Computer Vision
DOI 10.1109/iccv.2007.4409069
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Abstract

In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.

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Crossref · 10.1109/iccv.2007.4409069

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2 of 2 references verified0 citation errors · 0 not checkable

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  • Accelerated growth of facial structure in childhood and adolescence increases task difficulty for humans, as previously shown for machine recognition.

    “This suggests that accelerated growth of facial structure in childhood and adolescence [24, 25] increases task difficulty for humans; as has previously been shown for machine recognition [27].”

    From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [27]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 24

  1. 10.1006/cviu.1997.0549 · DOI
  2. 10.1109/34.993553 · DOI
  3. 10.1109/TSMCB.2003.817091 · DOI
  4. 10.1109/TPAMI.2002.1008382 · DOI
  5. 10.1109/AFGR.1998.670921 · DOI
  6. 10.1109/34.598227 · DOI
  7. phillips, 1999, Support vector machines applied to face recognition, NIPS, 2, 803
  8. 10.1037/0096-1523.1.4.374 · DOI
  9. 10.1109/TIP.2006.881993 · DOI
  10. boissieux, 2000, Simulation of skin aging and wrinkles with cosmetics insight, Eur Wksp Animation & Simulation, 15
  11. zhou, 2005, Image Based Regression Using Boosting Method, ICCV, 1, 541
  12. 10.1109/CVPR.2000.855827 · DOI
  13. igarashi, 2005, The Appearance of Human Skin, Technical Report Dept of Computer Science
  14. hammond, 2005, Nonlinear Image Representation via Local Multiscale Orientation, Courant Institute Technical Report TR2005–875
  15. 10.1007/BF00375127 · DOI
  16. 10.1046/j.1365-2133.2002.04874.x · DOI
  17. 10.5244/C.13.54 · DOI
  18. 10.1109/CVPR.2006.187 · DOI · Paper page
  19. 10.1364/JOSAA.16.002169 · DOI
  20. 10.1109/CVPR.2007.383055 · DOI
  21. 10.1007/978-1-4757-2440-0 · DOI · Paper page
  22. 10.1145/882262.882344 · DOI
  23. 10.1145/954339.954342 · DOI
  24. 10.1109/CVPR.2001.990935 · DOI

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Metadata

Authors
Ling, H., Soatto, S., Ramanathan, N., & Jacobs, D. W.
Venue
2007 IEEE 11th International Conference on Computer Vision
Publisher
IEEE
Published
2007
Pages
1-8
DOI
10.1109/iccv.2007.4409069
Cited by
101
References
2

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