Impact of Facial Cosmetics on Automatic Gender and Age Estimation Algorithms

Cunjian Chen, Antitza Dantcheva, Arun Ross

Abstract

Recent research has established the negative impact of facial cosmetics on the matching accuracy of automated face recognition systems. In this paper, we analyze the impact of cosmetics on automated gender and age estimation algorithms. In this regard, we consider the use of facial cosmetics for (a) gender spoofing where male subjects attempt to look like females and vice versa, and (b) age alteration where female subjects attempt to look younger or older than they actually are. While such transformations are known to impact human perception, their impact on computer vision algorithms has not been studied. Our findings suggest that facial cosmetics can potentially be used to confound automated gender and age estimation schemes.

References

  1. Bekios-Calfa, J., Buenaposada, J. M., and Baumela, L. (2011). Revisiting linear discriminant techniques in gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4):858-864.
  2. Chen, C., Dantcheva, A., and Ross, A. (2013). Automatic facial makeup detection with application in face recognition. In IAPR International Conference on Biometrics (ICB).
  3. Chen, C. and Ross, A. (2011). Evaluation of gender classification methods on thermal and near-infrared face images. In International Joint Conference on Biometrics (IJCB), pages 1-8.
  4. Dantcheva, A., Chen, C., and Ross, A. (2012). Can facial cosmetics affect the matching accuracy of face recognition systems? In IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).
  5. Dellinger, K. and Williams, C. L. (1997). Makeup at work: Negotiating appearance rules in the workplace. Gender and Society, 11(2):151-177.
  6. Eckert, M.-L., Kose, N., and Dugelay, J.-L. (2013). Facial cosmetics database and impact analysis on automatic face recognition. In IEEE International Workshop on Multimedia Signal Processing.
  7. Feng, R. and Prabhakaran, B. (2012). Quantifying the makeup effect in female faces and its applications for age estimation. In IEEE International Symposium on Multimedia, pages 108-115.
  8. Guo, G. and Mu, G. (2011). Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 657-664.
  9. Jain, A. K., Dass, S. C., and Nandakumar, K. (2004). Can soft biometric traits assist user recognition? In Proceedings of SPIE Defense and Security Symposium, volume 5404, pages 561-572.
  10. Klare, B., Burge, M. J., Klontz, J. C., Bruegge, R. W. V., and Jain, A. K. (2012). Face recognition performance: Role of demographic information. IEEE Transactions on Information Forensics and Security, 7(6):1789- 1801.
  11. Klontz, J., Klare, B., Klum, S., Taborsky, E., Burge, M., and Jain, A. K. (2013). Open source biometric recognition. In IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).
  12. Kwan, S. and Trautner, M. N. (2009). Beauty work: Individual and institutional rewards, the reproduction of gender, and questions of agency. Sociology Compass, 3(1):49-71.
  13. Li, B., Lian, X.-C., and Lu, B.-L. (2012). Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing, 76(1):18-27.
  14. Makinen, E. and Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3):541-547.
  15. Nash, R., Fieldman, G., Hussey, T., Leveque, J.-L., and Pineau, P. (2006). Cosmetics: They influence more than caucasian female facial attractiveness. Journal of Applied Social Psychology, 36(2):493-504.
  16. Reid, D., Samangooei, S., Chen, C., Nixon, M., and Ross, A. (2013). Soft biometrics for surveillance: An overview. In Handbook of Statistics, volume 31.
  17. Ricanek Jr., K. and Tesafaye, T. (2006). Morph: A longitudinal image database of normal adult age-progression. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 341-345.
  18. Rice, A., Phillips, P. J., Natu, V., An, X., and O'Toole, A. J. (2013). Unaware person recognition from the body when face identification fails. Psychological Science, (Published Online before Print, September 25).
  19. Russell, R. (2009). A sex difference in facial contrast and its exaggeration by cosmetics. Perception, 38(8):1211- 1219.
  20. Russell, R. (2010). Why cosmetics work. New York: Oxford University Press.
  21. Ueda, S. and Koyama, T. (2010). Influence of make-up on facial recognition. Perception, 39:260-264.
Download


Paper Citation


in Harvard Style

Chen C., Dantcheva A. and Ross A. (2014). Impact of Facial Cosmetics on Automatic Gender and Age Estimation Algorithms . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 182-190. DOI: 10.5220/0004746001820190


in Bibtex Style

@conference{visapp14,
author={Cunjian Chen and Antitza Dantcheva and Arun Ross},
title={Impact of Facial Cosmetics on Automatic Gender and Age Estimation Algorithms},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={182-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004746001820190},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Impact of Facial Cosmetics on Automatic Gender and Age Estimation Algorithms
SN - 978-989-758-004-8
AU - Chen C.
AU - Dantcheva A.
AU - Ross A.
PY - 2014
SP - 182
EP - 190
DO - 10.5220/0004746001820190