Advertisement

A hybrid technique for gender classification with SLBP and HOG features

  • M. Annalakshmi
  • S. Mohamed Mansoor Roomi
  • A. Sheik Naveedh
Article
  • 45 Downloads

Abstract

Gender classification from facial images plays a significant role in biometric technology viz. gender medicine, surveillance, electronic banking system and human computer interaction. However, it has many challenges due to variations of pose, expression, aging, race, make-up, occlusion and illumination. In the proposed system, spatially enhanced local binary pattern (SLBP) and histogram of oriented gradients (HOG) are extracted to classify the human gender with SVM classifier. This hybrid feature selection has increased the power of the proposed system due to its representation of texture micro-patterns and local shape by capturing the edge or gradient structure form the image. The gender classification accuracy is studied by using the local feature representation of the face images separately and also these features are concatenated to provide a better recognition rate. The combination of two different local descriptors provides good representation of face image and this is given to SVM classifier which classifies as male or female. Also, the proposed work is compared with other two traditional classifiers such as k-nearest neighbor and sparse representation classifier. The performance was evaluated on FERET and LFW database. The highest classification accuracy 99.1% is achieved on FERET database and 95.7% is achieved on LFW database by applying cubic SVM with fusion of SLBP and HOG features.

Keywords

Gender medicine Gender classification Face image processing Spatial local binary pattern Histogram of oriented gradients 

References

  1. 1.
    Feng, L., Yingxiao, W., Yan, Z.: Human gender classification: a review. Int. J. Biom. 8(3/4), 275–300 (2016)CrossRefGoogle Scholar
  2. 2.
    Brunelli, R., Poggio, T.: Hyperbf networks for gender classification. In: Proceedings of the DARPA Image Understanding Workshop, pp. 311–314 (1992)Google Scholar
  3. 3.
    Buchala, S., Davey, N., Frank, R., Gale, T., Loomes, M., Kanargard, W.: Gender classification of face images: the role of global and feature-based information Neural in-formation processing, pp. 763–768. Springer, New York (2004)Google Scholar
  4. 4.
    Andreu, Y., Garcia-Sevilla, P., Mollineda, R.A.: Face gender classification: a statistical study when neutral and distorted faces are combined for training and testing purposes. Image Vis. Comput. 32(1), 27–36 (2014).  https://doi.org/10.1016/j.imavis.2013.11.001 CrossRefGoogle Scholar
  5. 5.
    Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: Sex-net: a neural network identifies sex from human faces. In: Proceedings of Advances in neural information processing systems, pp. 572–577 (1990)Google Scholar
  6. 6.
    Cottrell, G.W., Metcalfe, J.: Emath: face, emotion, and gender recognition using holons. In: Proceedings of Advances in Neural Information Processing Systems, pp. 564–571 (1990)Google Scholar
  7. 7.
    Tamura, S., Kawai, H., Mitsumoto, H.: Male/female identification from 86 very low resolution face images by neural network. Pattern Recognit. 29(2), 331–335 (1996)CrossRefGoogle Scholar
  8. 8.
    Wiskott, L., Fellous, J.M., Kruger, N., der Mals-burg, C.V.: Face recognition and gender determination. In: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, pp. 92–97 (1995)Google Scholar
  9. 9.
    BenAbdelkader, C., Griffin, P.: A local region-based approach to gender classi cation from face images. In: IEEE Computer Society Conference on Computer vision and pattern recognition (CVPR)-workshops, p. 52 (2005)Google Scholar
  10. 10.
    Li, B., Lian, X.C., Lu, B.L.: Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing, 76(1), 1–10 (2011).  https://doi.org/10.1016/j.neucom.2011.01.028
  11. 11.
    Dreuw, P., Steingrube, P., Hanselmann, H., Ney, H.: Surf-face: face recognition under viewpoint consistency constraints. In: Proceedings of the British Machine Vision Conference (BMVC2007)Google Scholar
  12. 12.
    Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of sift features for face authentication. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW06), p. 35 (2006)Google Scholar
  13. 13.
    Zhang, S., Wang, H., Huang, W., You, Z.: Plant diseased leaf segmentation and recognition by fusion of super-pixel, k-means and phog. Optik-Int. J. Light Electron Opt. 157, 866–872 (2017)CrossRefGoogle Scholar
  14. 14.
    Albiol, A., Monzo, D., Martin, A., Sastre, J.: Face recognition using hog-ebgm. Pattern Recognit. Lett. 29(10), 1537–1543 (2008)CrossRefGoogle Scholar
  15. 15.
    Hadid, A., Pietikinen, M.: Combining motion and appearance for gender classification from video sequences. Pattern Recognit. 42(11), 2818–2827 (2009)CrossRefGoogle Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Wang, J., Li, J., Yau, W., Sung, E.: Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: IEEE computer society conference on Computer vision and pattern recognition workshops (CVPRW), pp. 96–102 (2010)Google Scholar
  18. 18.
    Wang, J., Li, J., Lee, C., Yau, W.: Dense sift and gabor descriptors-based face representation with applications to gender recognition. Im: 11th International Conference on Control Automation Robotics and Vision (ICARCV), pp. 1860–1864 (2010)Google Scholar
  19. 19.
    Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal. Mach. Intell. IEEE Trans. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  20. 20.
    Lian, H., Lu, B.: Multi-view gender classification using local binary patterns and support vector machines. Adv. Neural Netw. 2006, 2818–2827 (2006)Google Scholar
  21. 21.
    Yang, Z., Ai, H.: Demographic Classification with Local Binary Patterns. In: Lee, S.W., Li, S.Z. (eds.) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol. 4642, pp. 464–473. Springer, Berlin, Heidelberg (2007)Google Scholar
  22. 22.
    Alexandre, L.: Gender recognition: a multiscale decision fusion approach. Pattern Recognit. Lett. 31(11), 1422–1427 (2010)CrossRefGoogle Scholar
  23. 23.
    Ahonen, T., Hadid, A., Pietikinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  24. 24.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Int. Conf. Comput. Vis. Pattern Recognit. 2, 886–893 (2005)Google Scholar
  25. 25.
    Zhu, Q., Avidan, S., Yeh, M., Cheng, K.: Fast human detection using a cascade of histograms of oriented gradients. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1491–1498 (2006)Google Scholar
  26. 26.
    Dollar, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Proceedings of the British Machine Vision Conference, pp. 1–11 (2010)Google Scholar
  27. 27.
    Deniz, O., Bueno, G., Salido, J., Torre, F.D.L.: Face recognition using histograms of oriented gradients. Pattern Recognit. Lett. 32(12), 1598–1603 (2011)CrossRefGoogle Scholar
  28. 28.
    Felzenszwalb, P., Girshick, R., McAllester, D.: Cascade object detection with deformable part models. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 2241–2248 (2010)Google Scholar
  29. 29.
    Dahmane, M., Meunier, J.: Emotion recognition using dynamic grid-based hog features. In: Proceedings of the IEEE International Conference Automatic Face and Gesture Recognition, pp. 884–888 (2011)Google Scholar
  30. 30.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  31. 31.
    Chen, Q., Zhang, G., Yang, X., Li, S., Li, Y., Wang, H.H.: Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed. Tools Appl. 23, 1–24 (2017)Google Scholar
  32. 32.
    Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  33. 33.
  34. 34.
    http://vis-www.cs.umass.edu/lfw/. Accessed 7 Jun 2017
  35. 35.
    Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust. Comput. 20(2), 1517–1525 (2017)CrossRefGoogle Scholar
  36. 36.
    Lu, L., Shi, P.: Fusion of multiple facial regions for expression-invariant gender classification. IEICE Electron Expr. 6(10), 587–593 (2009).  https://doi.org/10.1587/elex.6.587
  37. 37.
    Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)CrossRefGoogle Scholar
  38. 38.
    Moghaddam, B., Yang, M.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)CrossRefGoogle Scholar
  39. 39.
    Mkinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)CrossRefGoogle Scholar
  40. 40.
    Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognit. Lett. 33(4), 431–437 (2012).  https://doi.org/10.1016/j.patrec.2011.05.016 CrossRefGoogle Scholar
  41. 41.
    Dago-Casas, P., Gonzalez-Jimenez, D., Yu, L.L., Alba-Castro, J.L.: Single-and cross-database benchmarks for gender classification under unconstrained settings. In: IEEE international conference on Computer vision workshops (ICCV Workshops), pp. 2152–2159 (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • M. Annalakshmi
    • 1
  • S. Mohamed Mansoor Roomi
    • 2
  • A. Sheik Naveedh
    • 3
  1. 1.Department of ECESethu Institute of TechnologyKariapattiIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia
  3. 3.Department of MechatronicsThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations