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International Symposium on Visual Computing

ISVC 2018: Advances in Visual Computing pp 157–166Cite as

Gender Classification Based on Facial Shape and Texture Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

Abstract

This paper seeks to improve gender classification accuracy by fusing shape features, the Active Shape Model with the two appearance based methods, the Local Binary Pattern (LBP) and Local Directional Pattern (LDP). A gender classification model based on the fusion of appearance and shape features is proposed. The experimental results show that the fusion of the LBP and LDP with the Active Shape Model improved the gender classification accuracy rate to 94.5% from 92.8% before fusion.

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References

  1. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. Proc. Mach. Learn. Res. 81, 1–15 (2018)

    Google Scholar 

  2. Cheung, Y.-m., Deng, J.: Ultra local binary pattern for image texture analysis. In: Secutrity, Pattern Analysis and Cybernetics (2014)

    Google Scholar 

  3. Angulu, R., Tapamo, J., Adewum, A.: Human age estimation using multifrequency biologically inspired features (mf-bif). In: IEEE AFRICON 2017 (2017)

    Google Scholar 

  4. Wang, Wei, He, Feixiang, Zhao, Qijun: Facial ethnicity classification with deep convolutional neural networks. In: You, Zhisheng, Zhou, Jie, Wang, Yunhong, Sun, Zhenan, Shan, Shiguang, Zheng, Weishi, Feng, Jianjiang, Zhao, Qijun (eds.) CCBR 2016. LNCS, vol. 9967, pp. 176–185. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46654-5_20

    Chapter  Google Scholar 

  5. Boyseens, A., Viriri, S.: Component-based ethnicity identification from facial images. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 293–303. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_26

    Chapter  Google Scholar 

  6. Luoh, L., Huang, C.: International coriference on system science and engineering image processing based emotion recognition. In: 2010 International Coriference on System Science and Engineering (2010)

    Google Scholar 

  7. Jain, A.K., Dass, S.C., Nandakumar, K.: Soft biometric traits for personal recognition systems. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 731–738. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25948-0_99

    Chapter  Google Scholar 

  8. Hoffmeyer-Zlotnik, J.H.P., Wolf, C. (eds.): Advances in Cross-National Comparison A European Working Book for Demographic and Socio-Economic Variables. Springer, New York (2003). https://doi.org/10.1007/978-1-4419-9186-7

    Book  Google Scholar 

  9. Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Process. 18(8) (2008)

    Google Scholar 

  10. Yang, W., Chen, C., Ricanek, K., Sun, C.: Gender classification via global-local features fusion. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 214–220. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25449-9_27

    Chapter  Google Scholar 

  11. Mozaffari, S., Behravan, H., Akbari, R.: Gender classification using single frontal image per person: combination of appearance and geometric based features. In: 20th International Conference on Pattern Recognition, ICPR 2010 (2010)

    Google Scholar 

  12. Chumerin, N., Hulle, M.M.V.: Comparison of two feature extraction methods based on maximization of mutual information. In: 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing (2006)

    Google Scholar 

  13. Lian, X.-C., Lu, B.-L.: Gender classification by combining facial and hair information. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 647–654. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03040-6_79

    Chapter  Google Scholar 

  14. Ziyi Xu, L.L., Shi, P.: A hybrid approach to gender classification from face images. In: 2008 IEEE (2008)

    Google Scholar 

  15. Lapedriza, A., Masip, D., Vitria, J.: Are external face features useful for automatic face classification? In: CVPR 2005, vol. 3, pp. 151–157 (2005)

    Google Scholar 

  16. Cao, L., Dikmen, M., Fu, Y.: Gender recognition from body. In: MM 08 Proceedings of the 16th ACM International Conference on Multimedi (2008)

    Google Scholar 

  17. Jabid, K.M., Chae, O.: Local directional pattern for face recognition. In: International Conference on Consumer Electronics (2010)

    Google Scholar 

  18. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape modelstheir training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)

    Article  Google Scholar 

  19. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0054760

    Chapter  Google Scholar 

  20. Lakshmiprabha, N.: Face image analysis using AAM, Gabor, LBP and WD features for gender age, expression and ethnicity classification. Comput. Vis. Pattern Recognit. (cs.CV) (2016)

    Google Scholar 

  21. Viola, P., Jones, M.: Robust real-time object detection. In: Second International Workshop on Statistical and Computational Theories of Vision-Modelling, Learning, Computing and Sampling (2001)

    Google Scholar 

  22. Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1) (2002)

    Google Scholar 

  23. Jabid, T., Kabir, H., Chae, O.: Robust facial expression recognition based on local directional pattern. ETRI J. (2010)

    Google Scholar 

  24. Cootes, T., Edwards, G., Taylor, C.: Comparing active shape models with active appearance models. In: British Machine Vision (1999)

    Google Scholar 

  25. Xu, Z., Lu, L., Shi, P.: A hybrid approach to gender classification from face images. In: 2008 19th International Conference on Pattern Recognition (2008)

    Google Scholar 

  26. Jadhao, V., Holambe, R.S.: Feature extraction and dimensionality reduction using radon and fourier transforms with application to face recognition. In: Conference on Computational Intelligence and Multimedia Applications (2007)

    Google Scholar 

  27. Pang, Y., Yuan, Y., Li, X.: Effective feature extraction in a high dimensinal space. IEEE Trans. Syst. (2008)

    Google Scholar 

  28. Yang, M.H., Moghaddam, B.: Support vector machines for visual gender classification. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Maths, Statistics and Computer Science, University of Kwazulu Natal, Durban, South Africa

    Mayibongwe H. Bayana, Serestina Viriri & Raphael Angulu

Authors
  1. Mayibongwe H. Bayana
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  2. Serestina Viriri
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  3. Raphael Angulu
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Cite this paper

Bayana, M.H., Viriri, S., Angulu, R. (2018). Gender Classification Based on Facial Shape and Texture Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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