Skip to main content
SpringerLink
Log in

Gender Classification Techniques: A Review

  • Conference paper
Advances in Computer Science, Engineering & Applications

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 166))

Abstract

Face is one of the most important biometric traits. By analyzing the face we get a lot of information such as age, gender, ethnicity, identity, expression, etc. A gender classification system uses face of a person from a given image to tell the gender (male/female) of the given person. A successful gender classification approach can boost the performance of many other applications including face recognition and smart human-computer interface. This paper illustrates the general processing steps for gender classification based on frontal face images. In this study, several techniques used in various steps of gender classification, i.e. feature extraction and classification, are also presented and compared.

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

Access this chapter

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Golomb, B.A., Lawerence, D.T., Sejnowski, T.J.: Sexnet: A Neural Network Identifies Sex from Human Faces. In: Advances in Neural Information Processing System, pp. 572–577 (1991)

    Google Scholar 

  2. Brunelli, R., Poggio, T.: Hyperbf Networks for Gender Classification. In: Proc. DARPA Image Understanding Workshop, pp. 311–314 (1992)

    Google Scholar 

  3. Wiskott, L., Fellous, J.M., Krüger, N., Von der Malsburg, C.: Face Recognition and Gender Determination. In: Proc. of Int. Workshop of Automatic Face and Gesture Recognition, pp. 92–97 (1995)

    Google Scholar 

  4. Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. of 13th Int. Conf. on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  5. Tamura, S., Kawai, H., Mitsumoto, H.: Male/Female Identification from 8 *6 Low Resolution Face Images by Neural Networks. Pattern Recognition 29(2), 331–335 (1996)

    Article  Google Scholar 

  6. Lyons, M., Budynek, J., Plante, A., Akamatsu: Classifying Facial Attributes Using a 2D Gabor Wavelet Representation and Discriminate Analysis. In: Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 202–207 (2000)

    Google Scholar 

  7. Shakhnarovich, G., Viola, P., Moghaddam, B.: A Unified Learning Framework for Real Time Detection and Classification. In: IEEE Conf. on AFG (2002)

    Google Scholar 

  8. Moghaddam, B., Yang, M.H.: Gender Classification with Support Vector Machines. IEEE Trans. on PAMI 24(5), 707–711 (2002)

    Article  Google Scholar 

  9. Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic Feature Subset Selection for Gender Classification: A Comparison Study. In: Proc. of IEEE Workshop on Applications of Computer Vision, pp. 165–170 (2002)

    Google Scholar 

  10. Graf, A., Wichmann, F.: Gender Classification of Human Faces. In: Proc. of the Int. Workshop on Biologically Motivated Computer Vision, pp. 491–500 (2002)

    Google Scholar 

  11. Wu, B., Ai, H., Huang, C.: Real Time Gender Classification. In: 3rd Int. Symposium on Multi-spectral Image Processing and Pattern Recognition, pp. 498–503 (2003)

    Google Scholar 

  12. Buchala, S., Davey, N., Frank, R., Gale, T.: Dimensionality Reduction of Face Images for Gender Classification. Technical Report 408, Department of Computer Science, the University of Hertfordshire, UK (2004)

    Google Scholar 

  13. Jain, A., Huang, J.: Integrating Independent Components and Linear Discriminant Anal-ysis for Gender Classification. In: Proc. of the IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 159–163 (2004)

    Google Scholar 

  14. Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender Classification Based on Boosting Local Binary Pattern. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  15. Lian, H.C., Lu, B.L.: Multi-View Gender Classification Using Local Binary Patterns and Support Vector Machines. In: Proc. 3rd Int. Symposia. on Neural Networks, Chengdu, China, pp. 202–209 (2006)

    Google Scholar 

  16. Baluja, S., Rowley, A.H.: Boosting Sex Identification Performance. Int. J. of Computer Vision 71(1), 111–119 (2007)

    Article  Google Scholar 

  17. Samal, A., Subramani, V., Marx, D.: Analysis of Sexual Dimorphism in Human Faces. Visual Communication and Image Representation 18(6), 453–463 (2007)

    Article  Google Scholar 

  18. Lu, H., Lin, H.: Gender Recognition using Adaboosted Feature. In: 3rd Int. Conf. on Natural Computation (2007)

    Google Scholar 

  19. Makinen, E., Raisamo, R.: An Experimental Comparison of Gender Classification Methods. Pattern Recognition Letters 29(10), 1544–1556 (2008)

    Article  Google Scholar 

  20. Makinen, E., Raisamo, R.: Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces. IEEE Trans. on PAMI 30(3), 541–547 (2008)

    Article  Google Scholar 

  21. Lu, H., Yingjie, H., Yenwei, C., Deli, Y.: Automatic Gender Recognition Based on Pixel-Pattern Based Texture Feature. J. of Real-Time Image Processing, 109–116 (2008)

    Google Scholar 

  22. Xu, Z., Lu, L., Shi, P.: A Hybrid Approach to Gender Classification from Face Images. In: 19th Int. Conf. on Pattern Recognition, pp. 1–4 (2008)

    Google Scholar 

  23. Ramesha, K., Srikanth, N., Raja, K.B., Venugopal, K.R., Patnaik, L.M.: Advance Biometric Identification on Face, Gender and Age Recognition. In: Int’l Conf. on Advances in Recent Technologies in Communication and Computing, pp. 23–27 (2009)

    Google Scholar 

  24. Lu, L., Shi, P.: A Novel Fusion-Based Method for Expression-Invariant Gender Classification. In: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 1065–1068 (2009)

    Google Scholar 

  25. Xia, H., Hassan, U., Ian, P.: Gender Classification Based on 3D Face Geometry using SVM. In: Int’l Conf. on Cyber Worlds, pp. 114–118 (2009)

    Google Scholar 

  26. Ramesha, K., Srikanth, N., Raja, K.B., Venugopal, K.R., Patnaik, L.M.: Feature Extraction Based face Recognition, Gender and Age Classification. Int. J. of Computer Science and Engineering 2(1), 14–23 (2010)

    Google Scholar 

  27. Bui, L., Tran, D., Xu, H., Chetty, G.: Face Gender Recognition Based on 2D Principal Component Analysis and Support Vector Machine. In: IEEE 4th Int. Conf. on Network and System Security (2010)

    Google Scholar 

  28. Jabid, T., Kabil, H., Chae, O.: Gender Classification using LDP. In: IEEE Int. Conf. on Pattern Recognition, pp. 2162–2165 (2010)

    Google Scholar 

  29. Li, Y., Zhang, Y., Zhao, S.: Gender Classification with Support Vector Machines Based on Non-Tensor Prewavelets. In: 2nd Int. Conf. on Computer Research and Development, pp. 770–774 (2010)

    Google Scholar 

  30. Rai, P., Khanna, P.: Gender Classification Using Radon and Wavelet Transforms. In: IEEE 5th Int. Conf. on Industrial Information System, pp. 448–451 (2010)

    Google Scholar 

  31. Alexandre, L.: Gender Recognition: A Multiscale Decision Fusion Approach. Pattern Recognition Letter 31(1), 1422–1427 (2010)

    Article  MathSciNet  Google Scholar 

  32. Yan, C.: Face Image Gender Recognition Based on Gabor Transform and SVM. In: Shen, G., Huang, X. (eds.) ECWAC 2011, Part II. CCIS, vol. 144, pp. 420–425. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  33. Basmaci, E.S., Kaymakcioglu, U., Kurt, Z.: Comparison of Feature Extraction and Feature Selection Approaches to Decide whether a Face Image Belongs to a Male or a Female. In: IEEE 19th Int. Conf. on Signal Processing and Communications Applications, pp. 522–525 (2011)

    Google Scholar 

  34. Sonka, M., Hlavac, V.: Digital Image processing and Computer Vision. Cengage India Learning Private limited, New Delhi (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Design & Manufacturing, PDPM Indian Institute of Information Technology, Jabalpur, India

    Preeti Rai & Pritee Khanna

Authors
  1. Preeti Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pritee Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Southeastern Louisiana University, Hammond, USA

    David C. Wyld

  2. Mendel University, Brno, Czech Republic

    Jan Zizka

  3. Wireilla Net Solutions PTY Ltd, Melbourne, Australia

    Dhinaharan Nagamalai

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag GmbH Berlin Heidelberg

About this paper

Cite this paper

Rai, P., Khanna, P. (2012). Gender Classification Techniques: A Review. In: Wyld, D., Zizka, J., Nagamalai, D. (eds) Advances in Computer Science, Engineering & Applications. Advances in Intelligent and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30157-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30157-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30156-8

  • Online ISBN: 978-3-642-30157-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation