Gender Classification Techniques: A Review

  • Preeti Rai
  • Pritee Khanna
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


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.


Biometrics feature extraction classifier 


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  1. 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. 2.
    Brunelli, R., Poggio, T.: Hyperbf Networks for Gender Classification. In: Proc. DARPA Image Understanding Workshop, pp. 311–314 (1992)Google Scholar
  3. 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. 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. 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)CrossRefGoogle Scholar
  6. 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. 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. 8.
    Moghaddam, B., Yang, M.H.: Gender Classification with Support Vector Machines. IEEE Trans. on PAMI 24(5), 707–711 (2002)CrossRefGoogle Scholar
  9. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  15. 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. 16.
    Baluja, S., Rowley, A.H.: Boosting Sex Identification Performance. Int. J. of Computer Vision 71(1), 111–119 (2007)CrossRefGoogle Scholar
  17. 17.
    Samal, A., Subramani, V., Marx, D.: Analysis of Sexual Dimorphism in Human Faces. Visual Communication and Image Representation 18(6), 453–463 (2007)CrossRefGoogle Scholar
  18. 18.
    Lu, H., Lin, H.: Gender Recognition using Adaboosted Feature. In: 3rd Int. Conf. on Natural Computation (2007)Google Scholar
  19. 19.
    Makinen, E., Raisamo, R.: An Experimental Comparison of Gender Classification Methods. Pattern Recognition Letters 29(10), 1544–1556 (2008)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 31.
    Alexandre, L.: Gender Recognition: A Multiscale Decision Fusion Approach. Pattern Recognition Letter 31(1), 1422–1427 (2010)CrossRefMathSciNetGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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. 34.
    Sonka, M., Hlavac, V.: Digital Image processing and Computer Vision. Cengage India Learning Private limited, New Delhi (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Preeti Rai
    • 1
  • Pritee Khanna
    • 1
  1. 1.Design & ManufacturingPDPM Indian Institute of Information TechnologyJabalpurIndia

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