Human Visual System Based Framework For Gender Recognition

Cherinet G. Zewdie, Hubert Konik

2015

Abstract

A face reveals a great deal of information to a perceiver including gender. Humans use specific information (cue) from a face to recognize gender. The focus of this paper is to find out this cue when the Human Visual System (HVS) decodes gender of a face. The result can be used by a Computer Vision community to develop HVS inspired framework for gender recognition. We carried out a Pyscho-visual experiment to find which face region is most correlated with gender. Eye movements of 15 observers were recorded using an eye tracker when they performed gender recognition task under controlled and free viewing condition. Analysis of the eye movement shows that the eye region is the most correlated with gender recognition. We also proposed a HVS inspired automatic gender recognition framework based on the Psycho-visual experiment. The proposed framework is tested on FERET database and is shown to achieve a high recognition accuracy.

References

  1. Alexandre, L. A. (2010). Gender recognition: A multiscale decision fusion approach. Pattern Recognition Letters, 31(11):1422-1427.
  2. Andreu, Y. and Mollineda, R. A. (2008). The role of face parts in gender recognition. In Image Analysis and Recognition, pages 945-954. Springer.
  3. BrownU, E. and Perrett, D. (1993). What gives a face its gender. Perception, 22:829-840.
  4. Bruce, V., Burton, A. M., Hanna, E., Healey, P., Mason, O., Coombes, A., Fright, R., and Linney, A. (1993). Sex discrimination: how do we tell the difference between male and female faces? Perception.
  5. Buchala, S., Davey, N., Frank, R. J., Gale, T. M., Loomes, M. J., and Kanargard, W. (2004). Gender classification of face images: The role of global and featurebased information. In Neural Information Processing, pages 763-768. Springer.
  6. Gutta, S., Wechsler, H., and Phillips, P. J. (1998). Gender and ethnic classification of face images. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pages 194-199. IEEE.
  7. Kawano, T., Kato, K., and Yamamoto, K. (2004). A comparison of the gender differentiation capability between facial parts. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 1, pages 350-353. IEEE.
  8. Makinen, E. and Raisamo, R. (2008). Evaluation of gender classification methods with automatically detected and aligned faces. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(3):541-547.
  9. Moghaddam, B. and Yang, M.-H. (2002). Learning gender with support faces. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):707-711.
  10. Mouchetant-Rostaing, Y., Giard, M.-H., Bentin, S., Aguera, P.-E., and Pernier, J. (2000). Neurophysiological correlates of face gender processing in humans. European Journal of Neuroscience, 12(1):303-310.
  11. Ng, C. B., Tay, Y. H., and Goi, B. M. (2012). Vision-based human gender recognition: a survey. arXiv preprint arXiv:1204.1611.
  12. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  13. Phillips, P. J., Wechsler, H., Huang, J., and Rauss, P. J. (1998). The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing, 16(5):295-306.
  14. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
Download


Paper Citation


in Harvard Style

G. Zewdie C. and Konik H. (2015). Human Visual System Based Framework For Gender Recognition . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 254-260. DOI: 10.5220/0005176102540260


in Bibtex Style

@conference{icaart15,
author={Cherinet G. Zewdie and Hubert Konik},
title={Human Visual System Based Framework For Gender Recognition},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={254-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005176102540260},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Human Visual System Based Framework For Gender Recognition
SN - 978-989-758-074-1
AU - G. Zewdie C.
AU - Konik H.
PY - 2015
SP - 254
EP - 260
DO - 10.5220/0005176102540260