Fusion of Holistic and Part Based Features for Gender Classification in the Wild

  • Modesto Castrillón-SantanaEmail author
  • Javier Lorenzo-Navarro
  • Enrique Ramón-Balmaseda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


Gender classification (GC) in the wild is an active area of current research. In this paper, we focus on the combination of a holistic state of the art approach based on features extracted from the facial pattern, with patch based approaches that focus on inner facial areas. Those regions are selected for being relevant to the human system according to the psychophysics literature: the ocular and the mouth areas. The resulting proposed GC system outperforms previous approaches, reducing the classification error of the holistic approach roughly a \(30\%\).


Gender classification Local descriptors Score level fusion 


  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), December 2006Google Scholar
  2. 2.
    Alexandre, L.A.: Gender recognition: A multiscale decision fusion approach. Pattern Recognition Letters 31(11), 1422–1427 (2010)CrossRefGoogle Scholar
  3. 3.
    Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Robust gender recognition by exploiting facial attributes dependencies. Pattern Recognition Letters 36, 228–234 (2014)CrossRefGoogle Scholar
  4. 4.
    Castrillón-Santana, M., Lorenzo-Navarro, J., Ramón-Balmaseda, E.: Improving gender classification accuracy in the wild. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part II. LNCS, vol. 8259, pp. 270–277. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  5. 5.
    Castrillón-Santana, M., Lorenzo-Navarro, J., Ramn-Balmaseda, E.: Evaluation of periocular over face gender classification in the wild (under review)Google Scholar
  6. 6.
    Castrillón-Santana, M., Marsico, M.D., Nappi, M., Riccio, D.: MEG: Multi-Expert Gender classification in a demographics-balanced dataset. In: 18th International Conference on Image Analysis and Processing (2015)Google Scholar
  7. 7.
    Chen, H., Gallagher, A.C., Girod, B.: The hidden sides of names - face modeling with first name attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(9), 1860–1873 (2014)CrossRefGoogle Scholar
  8. 8.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)CrossRefGoogle Scholar
  9. 9.
    Dago-Casas, P., González-Jiménez, D., Long-Yu, L., Alba-Castro, J.L.: Single- and cross- database benchmarks for gender classification under unconstrained settings. In: Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies (2011)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C., Soatto, S., Tomasi, C. (eds.) International Conference on Computer Vision & Pattern Recognition, vol. 2, pp. 886–893, June 2005Google Scholar
  11. 11.
    Gallagher, A., Chen, T.: Understanding images of groups of people. In: Proc. CVPR (2009)Google Scholar
  12. 12.
    García-Olalla, O., Alegre, E., Fernández-Robles, L., González-Castro, V.: Local oriented statistics information booster (LOSIB) for texture classification. In: International Conference in Pattern Recognition (ICPR) (2014)Google Scholar
  13. 13.
    Gosselin, F., Schyns, P.G.: Bubbles: a technique to reveal the use of information in recognition tasks. Vision Research, 2261–2271 (2001)Google Scholar
  14. 14.
    Han, H., Jain, A.K.: Age, gender and race estimation from unconstrained face images. Tech. Rep. MSU-CSE-14-5. Michigan State University (2014)Google Scholar
  15. 15.
    Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. International Journal of Computer Vision Research 74(2), August 2007Google Scholar
  16. 16.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07–49. University of Massachusetts, Amherst, October 2007Google Scholar
  17. 17.
    Jia, S., Cristianini, N.: Learning to classify gender from four million images. Pattern Recognition Letters (2015)Google Scholar
  18. 18.
    Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognition 45(9), 3304–3316 (2012)CrossRefGoogle Scholar
  19. 19.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), October 2011Google Scholar
  20. 20.
    Ngan, M., Grother, P.: Face recognition vendor test (frvt) performance of automated gender classification algorithms. Tech. Rep. NIST IR 8052. Narional Institute of Standars and Technology, April 2015Google Scholar
  21. 21.
    Ren, H., Li, Z.N.: Gender recognition using complexity-aware local features. In: International Conference on Pattern Recognition (2014)Google Scholar
  22. 22.
    Shafey, L.E., Khoury, E., Marcel, S.: Audio-visual gender recognition in uncontrolled environment using variability modeling techniques. In: International Joint Conference on Biometrics (2014)Google Scholar
  23. 23.
    Shan, C.: Learning local binary patterns for gender classification on realworld face images. Pattern Recognition Letters 33, 431–437 (2012)CrossRefGoogle Scholar
  24. 24.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Tapia, J.E., Pérez, C.A.: Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of lbp, intensity and shape. IEEE Transactions on Information Forensics and Security 8(3), 488–499 (2013)CrossRefGoogle Scholar
  26. 26.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Modesto Castrillón-Santana
    • 1
    Email author
  • Javier Lorenzo-Navarro
    • 1
  • Enrique Ramón-Balmaseda
    • 1
  1. 1.Universidad de Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

Personalised recommendations