Local Gradient Increasing Pattern (LGIP) for Facial Representation and Gender Recognition

  • Lu Bing Zhou
  • Han Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

A robust facial representation is an essential component for gender classification. This paper introduces a new local feature, Local Gradient Increasing Pattern (LGIP), which expresses the local intensity increasing trend. A LGIP feature is to encode intensity increasing trends in 8 orientations at each pixel using signs of directional gradient responses, and overall increasing trend is assigned with a decimal label. A facial image is partitioned into overlapping regions from which LGIP histograms are obtained and concatenated into a single feature vector. Gender classification is carried out using SVM classifier based on the LGIP-based facial descriptor. We investigate the influence to recognition rates by two factors, image resolution and person-dependent/independent condition. Experiments are performed on two replicable image sets from CAS-PEAL and FERET databases, and the results show that our method achieves better performance than many other methods.

Keywords

gender classification local gradient increasing pattern facial representation support vector machine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The cas-peal large-scale chinese face database and baseline evaluations. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 38(1), 149–161 (2008)CrossRefGoogle Scholar
  3. 3.
    Gutta, S., Wechsler, H., Phillips, P.J.: Gender and ethnic classification of face images. In: 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 194–199 (1998)Google Scholar
  4. 4.
    Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Tech. rep. (2003), http://www.csie.ntu.edu.tw/cjlin/pa-pers/guide/guide.pdf
  5. 5.
    Jabid, T., Hasanul Kabir, M., Chae, O.: Gender classification using local directional pattern (ldp). In: 20th International Conference on Pattern Recognition (ICPR), pp. 2162–2165 (2010)Google Scholar
  6. 6.
    Makinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(3), 541–547 (2008)CrossRefGoogle Scholar
  7. 7.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  8. 8.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  9. 9.
    Sun, N., Zheng, W., Sun, C., Zou, C.-R., Zhao, L.: Gender Classification Based on Boosting Local Binary Pattern. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Ylioinas, J., Hadid, A., Pietikäinen, M.: Combining Contrast Information and Local Binary Patterns for Gender Classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 676–686. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lu Bing Zhou
    • 1
  • Han Wang
    • 1
  1. 1.School of Electrical & Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations