Skip to main content
SpringerLink
Log in

Multi-view gender classification using symmetry of facial images

  • ICONIP2010
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a multi-view gender classification system with a hierarchical framework using facial images as input. The front end of the framework is a classifier, which will properly divides the input images into several groups. To ease the data sparsity problem in the multi-view scenario, facial symmetry is used to reduce the number of views. Moreover, we adopt soft assignment when dividing the input data, which can reduce the errors caused by the boundary effect in hard assignment. Then for each group, we train a gender classifier, called an expert. These experts can be any commonly used classifiers, such as support vector machines or neural networks. In this step, facial components instead of the whole face are used to achieve higher robustness against variations caused by facial alignment, illumination and occlusions. Experimental results demonstrate that our framework significantly improves the performance.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Golomb B, Lawrence D, Sejnowski T (1990) Sexnet: a neural network identifies sex from human faces. Proc Adv Neural Inf Process Syst 3:572–579

    Google Scholar 

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

  3. Gutta S, Wechsler H (July 1999) Gender and ethnic classification of human faces using hybrid classifiers. In: Neural Networks, 1999. IJCNN ’99. International Joint Conference on, vol 6, pp 4084–4089

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

    Article  Google Scholar 

  5. Kim H, Kim D, Ghahramani Z, Bang S (2006) Appearance-based gender classification with gaussian processes. Pattern Recogn Lett 27(6):618–626

    Article  Google Scholar 

  6. Lian H, Lu B (2006) Multi-view gender classification using local binary patterns and support vector machines. Proceedings of the third international symposium on neural networks, pp 202–209

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

    Article  Google Scholar 

  8. Lian X, Lu B (2009) Gender classification by combining facial and hair information. Proceedings of advances in neuro-information processing, pp 647–654

  9. Xia B, Sun H, Lu B (2008) Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines. In: IEEE international joint conference on neural networks, pp 3388–3395

  10. Li B, Lian X, Lu B (2011) Gender classification by combining hair, clothing and face organ classifiers. to appear in Neuralcomputing

  11. Sugeno M (1974) Theory of fuzzy integrals and its applications

  12. Burton A, Bruce V, Dench N (1993) What’s the difference between men and women? Evidence from facial measurement. PERCEPTION-LONDON-22:153–153

    Article  Google Scholar 

  13. Fellous J (1997) Gender discrimination and prediction on the basis of facial metric information. Vis Res 37(14):1961–1973

    Article  Google Scholar 

  14. Tamura S, Kawai H, Mitsumoto H (1996) Male/female identification from 8 Ũ 6 very low resolution face images by neural network. Pattern Recogn Lett 29(2):331–335

    Google Scholar 

  15. Cottrell G, Metcalfe J (1990) EMPATH: Face, emotion, and gender recognition using holons. In: Proceedings of the 1990 conference on advances in neural information processing systems 3, Morgan Kaufmann Publishers Inc. 571

  16. Sun Z, Bebis G, Yuan X, Louis S (2003) Genetic feature subset selection for gender classification: a comparison study. In: Applications of computer vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, IEEE, pp 165–170

  17. Jain A, Huang J (2004) Integrating independent components and linear discriminant analysis for gender classification. In: Automatic face and gesture recognition, 2004. Proceedings. Sixth IEEE international conference on, IEEE, pp 159–163

  18. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. Tenth IEEE international conference on computer cision 1

  19. Wu B, Ai H, Huang C (2003) Lut-based adaboost for gender classification. Proceedings of international conference on audio-and video-based biometric person authentication, pp 104–110

  20. Sun N, Zheng W, Sun C, Zou C, Zhao L (2006) Gender classification based on boosting local binary pattern. Proceedings of 3rd International Symposium on Neural Networks, vol 2, pp 194–201

  21. Toews M, Arbel T (2009) Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. IEEE Trans Pattern Anal Mach Intell 31:1567–1581

    Article  Google Scholar 

  22. Takimoto H, Mitsukura Y, Fukumi M (2007) Robust gender and age estimation under varying facial pose. IEEJ Trans 127(7):1022–1029

    Google Scholar 

  23. Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with local binary patterns. Comput Vis Proc 3021:469–481

    Google Scholar 

  24. Ji Z, Lian X, Lu B (2009) Gender classification by fusion of face and hair feature. State of the Art in Face Recognition 215–230

  25. Ji Z, Lu B (2011) A support vector machine classifier with automatic confidence and its application to gender classification. To appear in Neuralcomputing

  26. Huang J, Shao X, Wechsler H (1998) Face pose discrimination using support vector machines (svm). In: Proceedings of 14th international conference on pattern recognition (ICPR98)

  27. Brown UE, Perrett D (1993) What gives a face its gender. Perception 22:829–840

    Article  Google Scholar 

  28. Cootes T, Taylor C, Cooper D, Graham J et al (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  29. Wu T, Lin C, Weng R (2004) Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res 5:975–1005

    MathSciNet  MATH  Google Scholar 

  30. Murofushi T, Sugeno M (1989) An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Syst 29(2):201–227

    Article  MathSciNet  MATH  Google Scholar 

  31. Vishwanathan SVN, Sun Z, Theera-Ampornpunt N, Varma M (December 2010) Multiple kernel learning and the SMO algorithm. In: Advances in Neural Information Processing Systems

  32. Makinen E, Raisamo R (2008) Evaluation of gender classification methods with automatically detected and aligned faces. In: IEEE Trans. Pattern Analysis and Machine Intelligence, vol 30(3), pp 541–547

  33. Gao W, Cao B, Shan S et al (2008) The cas-peal large-scale chinese face database and baseline evaluations. IEEE T Syst Man Cy A 38(1):149–161

    Google Scholar 

  34. Lian H, Lu B (2007) Multi-view gender classification using multi-resolution local binary patterns and support vector machines. Int J Neural Syst 17(6):479–487

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant No. 90820018), the National Basic Research Program of China (Grant No. 2009CB320901), and the Science and Technology Commission of Shanghai Municipality (Grant No. 09511502400).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Center for Brain-Like Computing and Machine Intelligence, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China

    Tian-Xiang Wu, Xiao-Chen Lian & Bao-Liang Lu

  2. MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China

    Bao-Liang Lu

Authors
  1. Tian-Xiang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Chen Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bao-Liang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bao-Liang Lu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, TX., Lian, XC. & Lu, BL. Multi-view gender classification using symmetry of facial images. Neural Comput & Applic 21, 661–669 (2012). https://doi.org/10.1007/s00521-011-0647-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0647-x

Keywords

Search

Navigation