Advertisement

Features Selection for the Most Accurate SVM Gender Classifier Based on Geometrical Features

  • Piotr MilczarskiEmail author
  • Zofia Stawska
  • Shane Dowdall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

In the paper, we have focused on the problem of choosing the best set of features in the task of gender classification/recognition. Choosing a minimum set of features, that can give satisfactory results is also important in the case where only a part of the face is visible. The minimum set of features can simplify the classification process to make it useful for mobile applications. Many authors have used SVM in facial classification and recognition problems, but there are not many works using facial geometry features in the classification neither in SVM. Almost all works are based on the appearance-based methods. In the paper, we show that the classifier constructed on the base of only two or three geometric facial features can give satisfactory (though not always optimal) results with accuracy 82% and positive predictive value 87%, also in incomplete facial images. We show that Matlab and Mathematica can produce very different SVMs given the same data.

Keywords

Geometric facial features Biometrics Gender classification Support Vector Machine 

References

  1. 1.
    Abdi, H., Valentin, D., Edelman, B., O’Toole, A.J.: More about the difference between men and women: evidence from linear neural network and principal component approach. Neural Comput. 7(6), 1160–1164 (1995)CrossRefGoogle Scholar
  2. 2.
    Alexandre, L.A.: Gender recognition: a multiscale decision fusion approach. Pattern Recogn. Lett. 31(11), 1422–1427 (2010)CrossRefGoogle Scholar
  3. 3.
    Alpaydin, E.: Combined 5 \(\times \) 2cv F test for comparing supervised classification learning algorithms. Neural Comput. 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  4. 4.
    Andreu, Y., Mollineda, R.A., Garcia-Sevilla, P.: Pattern Recognition and Image Analysis. LNCS, vol. 5524. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02172-5CrossRefGoogle Scholar
  5. 5.
    Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)CrossRefGoogle Scholar
  6. 6.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of 5th Annual Workshop on Computational Learning Theory COLT-1992, p. 144 (1992)Google Scholar
  7. 7.
    Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)CrossRefGoogle Scholar
  8. 8.
    Buchala, S., Loomes, M.J., Davey, N., Frank, R.J.: The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models. Int. J. Neural Syst. 15, 121–128 (2005)CrossRefGoogle Scholar
  9. 9.
    Burton, A.M., Bruce, V., Dench, N.: What’s the difference between men and women? Evidence from facial measurements. Perception 22, 153–176 (1993)CrossRefGoogle Scholar
  10. 10.
    Castrillon, M., Deniz, O., Hernandez, D., Dominguez, A.: Identity and gender recognition using the encara real-time face detector. In: Conferencia de la Asociacin Espaola para la Inteligencia Artificial, vol. 3 (2003)Google Scholar
  11. 11.
    Castrillon-Santana, M., Lorenzo-Navarro, J., Ramon-Balmaseda, E.: On using periocular biometric for gender classification in the wild. Pattern Recogn. Lett. 82, 181–9 (2016)CrossRefGoogle Scholar
  12. 12.
    Cortes, C., Vapnik, V.: Support-vector network. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  13. 13.
    Cottrell, G.W., Metcalfe, J.: EMPATH: face, emotion, and gender recognition using holons. In: Lippmann, R., Moody, J.E., Touretzky, D.S. (eds.) Proceedings of Advances in Neural Information Processing Systems (NIPS), vol. 3, pp. 564–571. Morgan Kaufmann (1990)Google Scholar
  14. 14.
    Demirkus, M., Toews, M., Clark, J.J., Arbel, T.: Gender classification from unconstrained video sequences. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 55–62 (2010)Google Scholar
  15. 15.
    Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10, 1895–1923 (1998)CrossRefGoogle Scholar
  16. 16.
    Fellous, J.M.: Gender discrimination and prediction on the basis of facial metric information. Vis. Res. 37(14), 1961–1973 (1997)CrossRefGoogle Scholar
  17. 17.
    Fok, T.H.C., Bouzerdoum, A.: A gender recognition system using shunting inhibitory convolutional neural networks. In: 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 5336–5341 (2006)Google Scholar
  18. 18.
    Hasnat, A., Haider, S., Bhattacharjee, D., Nasipuri, M.: A proposed system for gender classification using lower part of face image. In: Proceedings of International Conference on Information Processing, pp. 581–585 (2015)Google Scholar
  19. 19.
    Hassanat, A.B., Prasath, V.B.S., Al-Mahadeen, B.M., Alhasanat, S.M.M.: Classification and gender recognition from veiled-faces. Int. J. Biometr. 9(4), 347–364 (2017)CrossRefGoogle Scholar
  20. 20.
    Humanæ Project. http://humanae.tumblr.com. Accessed 15 Nov 2017
  21. 21.
    Jain, A., Huang, J., Fang, S.: Gender identification using frontal facial images. In: IEEE International Conference on Multimedia and Expo, ICME 2005, p. 4 (2005)Google Scholar
  22. 22.
    Kawano, T., Kato, K., Yamamoto, K.: An analysis of the gender and age differentiation using facial parts. In: IEEE International Conference on Systems Man and Cybernetics, vol. 4, pp. 3432–3436, 10–12 October 2005Google Scholar
  23. 23.
    Kompanets, L., Milczarski, P., Kurach, D.: Creation of the fuzzy three-level adapting brainthinker. In: 6th International Conference on Human System Interaction (HSI), pp. 459–465 (2013).  https://doi.org/10.1109/HSI.2013.6577865
  24. 24.
    Mäkinen, E., Raisamo, R.: An experimental comparison of gender classification methods. Pattern Recogn. Lett. 29, 1544–56 (2008)CrossRefGoogle Scholar
  25. 25.
    Martinez, A.M., Benavente, R.: The AR face database. CVC Technical report #24 (1998)Google Scholar
  26. 26.
    Merkow, J., Jou, B., Savvides, M.: An exploration of gender identification using only the periocular region. In: Proceedings of 4th IEEE International Conference on Biometrics Theory Applications and Systems BTAS, pp. 1–5 (2010)Google Scholar
  27. 27.
    Milczarski, P.: A new method for face identification and determining facial asymmetry. In: Katarzyniak, R., et al. (eds.) Semantic Methods for Knowledge Management and Communication. SCI, vol. 381, pp. 329–340. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23418-7_29CrossRefGoogle Scholar
  28. 28.
    Milczarski, P., Kompanets, L., Kurach, D.: An approach to brain thinker type recognition based on facial asymmetry. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 643–650. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13208-7_80CrossRefGoogle Scholar
  29. 29.
    Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)CrossRefGoogle Scholar
  30. 30.
    Muldashev, E.R.: Whom Did We Descend From? OLMA Press, Moscow (2002). (in Russian)Google Scholar
  31. 31.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  32. 32.
    Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: Proceedings of International Conference on Automatic Face and Gesture Recognition (FGR 2002), pp. 14–21. IEEE (2002)Google Scholar
  33. 33.
    Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic feature subset selection for gender classification: a comparison study. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV 2002), pp. 165–170 (2002)Google Scholar
  34. 34.
    Vapnik, V.N., Kotz, S.: Estimation of Dependences Based on Empirical Data. Springer, New York (2006).  https://doi.org/10.1007/0-387-34239-7CrossRefGoogle Scholar
  35. 35.
    Wang, J.G., Li, J., Lee, C.Y., Yau, W.Y.: Dense SIFT and Gabor descriptors-based face representation with applications to gender recognition. In: 11th International Conference on Control Automation Robotics & Vision (ICARCV), no. December, pp. 1860–1864 (2010)Google Scholar
  36. 36.
    Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 456–463. Springer, Heidelberg (1997).  https://doi.org/10.1007/3-540-63460-6_150CrossRefGoogle Scholar
  37. 37.
    Yamaguchi, M., Hirukawa, T., Kanazawa, S.: Judgment of gender through facial parts. Perception 42, 1253–1265 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Physics and Applied InformaticsUniversity of LodzLodzPoland
  2. 2.Department of Visual and Human Centred ComputingDundalk Institute of TechnologyDundalkIreland

Personalised recommendations