Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Biometrics pp 832–837Cite as

Gender Classification

  • Reference work entry
  • First Online:

Synonyms

Gender recognition; Sex classification; Gender prediction

Definition

Gender classification is to determine a person’s gender, e.g., male or female, based on his or her biometric cues. Usually facial images are used to extract features and then a classifier is applied to the extracted features to learn a gender recognizer. It is an active research topic in Computer Vision and Biometrics fields. The gender classification result is often a binary value, e.g., 1 or 0, representing either male or female. Gender recognition is essentially a two-class classification problem. Although other biometric traits could also be used for gender classification, such as gait, face-based approaches are still the most popular for gender discrimination.

Introduction

A sex difference is a distinction of biological and/or physiological characteristics associated with either males or females of a species. These can be of several types, including direct and indirect. Direct is the direct result of...

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. S. Baluja, H.A. Rowley, Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)

    Google Scholar 

  2. R. Brunelli, T. Poggio, Hyperbf networks for gender classification, in Proceedings of the DARPA Image Understanding Workshop, San Diego, 1992, pp. 311–314

    Google Scholar 

  3. FGNET, The FG-NET aging database (2002), http://www.fgnet.rsunit.com/

  4. Y. Freund, R. Schapire, Experiments with a new boosting algorithm, in Proceedings of the Thirteenth International Conference on Machine Learning, Bari, 1996, pp. 148–156

    Google Scholar 

  5. B. Golomb, D. Lawrence, T. Sejnowski, Sexnet: a neural network identifies sex from human faces, in Advances in Neural Information Processing Systems, ed. by R.P. Lippmann, J.E. Moody, D.S. Touretzky, vol. 3 (Morgan Kaufmann Publishers, San Mateo, 1991), pp. 572–577

    Google Scholar 

  6. A. Graf, F. Wichmann, Gender classification of human faces, in International Workshop on Biologically Motivated Computer Vision, Tübingen, 2002, pp. 491–500

    Google Scholar 

  7. G. Guo, Human age estimation and sex classification, in Video Analytics for Business Intelligence, ed. by C. Shan et al. SCI 409 (Springer, Berlin/New York, 2012), pp. 101–131

    Google Scholar 

  8. G. Guo, C. Dyer, Y. Fu, T.S. Huang, Is gender recognition influenced by age? in IEEE International Workshop on Human-Computer Interaction, in Conjunction with ICCV’09, Kyoto, Japan, 2009, pp. 2032–2039

    Google Scholar 

  9. S. Gutta, H. Wechsler, P. Phillips, Gender and ethnic classification, in Proceedings of the IEEE International Workshop on Automatic Face and Gesture Recognition, Nara, 1998, pp. 194–199

    Google Scholar 

  10. D. Haig, The inexorable rise of gender and the decline of sex: social change in academic titles, 1945–2001. Arch. Sex. Behav. 33(2), 87–96 (2004)

    Google Scholar 

  11. A. Jain, J. Huang, Integrating independent component analysis and linear discriminant analysis for gender classification, in IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, 2004

    Google Scholar 

  12. E. Makinen, R. Raisamo, Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)

    Google Scholar 

  13. B. Moghaddam, M.-H. Yang, Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)

    Google Scholar 

  14. Sex differences in humans, in http://en.wikipedia.org/wiki/Sex_differences_in_humans

  15. G. Shakhnarovich, P. Viola, B. Moghaddam, A unified learning framework for real time face detection and classification, in International Conference on Automatic Face and Gesture Recognition, Washington, DC, 2002

    Google Scholar 

  16. V.N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998)

    MATH  Google Scholar 

  17. B. Wu, H. Ai, C. Huang, S. Lao, Lut-based adaboost for gender classification, in International Conference on Audio and Video-Based Person Authentication, Guildford, 2003

    Google Scholar 

  18. X. Xu, T. Huang, SODA-boosting and its application to gender recognition, in AMFG 2007, Rio de Janeiro, ed. by S.K. Zhou, W. Zhao, X. Tang, S. Gong. LNCS, vol. 4778 (Springer, Heidelberg, 2007), pp. 193–204

    Google Scholar 

  19. Z. Yang, H. Ai, Demographic classification with local binary patterns, in International Conference on Biometrics, Seoul, 2007, pp. 464–473

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

    Guodong Guo

Authors
  1. Guodong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Biometrics and Security, Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

  2. Departments of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Anil K. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Guo, G. (2015). Gender Classification. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_9176

Download citation

Publish with us

Policies and ethics

Search

Navigation