Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis

  • Gaurav Sharma
  • Sibt ul Hussain
  • Frédéric Jurie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)


This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been shown recently that small local pixel pattern distributions can be highly discriminative. Motivated by such works, the proposed model employs higher-order statistics of local non-binarized pixel patterns for the image description. Hence, in addition to being remarkably simple, it requires neither any user specified quantization of the space (of pixel patterns) nor any heuristics for discarding low occupancy volumes of the space. This leads to a more expressive representation which, when combined with discriminative SVM classifier, consistently achieves state-of-the-art performance on challenging texture and facial analysis datasets outperforming contemporary methods (with similar powerful classifiers).


Face Image Gaussian Mixture Model Local Binary Pattern Facial Expression Recognition Linear Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC 2010) (2010), Results
  2. 2.
    Leung, T.J., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV 43, 29–44 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)Google Scholar
  4. 4.
    Cula, O.G., Dana, K.J.: Compact representation of bidirectional texture functions. In: CVPR (2001)Google Scholar
  5. 5.
    Zhu, S.C., Wu, Y., Mumford, D.: Filters, random-fields and maximum-entropy (FRAME): Towards a unified theory for texture modeling. IJCV 27, 107–126 (1998)CrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: CVPR (2003)Google Scholar
  8. 8.
    Pietikinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
  9. 9.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. PAMI 28 (2006)Google Scholar
  10. 10.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. TIP 19, 1635–1650 (2010)MathSciNetGoogle Scholar
  11. 11.
    Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: A robust local image descriptor. PAMI 32, 1705–1720 (2010)CrossRefGoogle Scholar
  12. 12.
    Valkealahti, K., Oja, E.: Reduced multidimensional co-occurence histograms in texture classification. PAMI 20, 90–94 (1998)CrossRefGoogle Scholar
  13. 13.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)Google Scholar
  14. 14.
    Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV (2005)Google Scholar
  15. 15.
    Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: AFGR (1998)Google 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. Technical Report 07-49, University of Massachusetts, Amherst (2007)Google Scholar
  17. 17.
    Liu, L., Fieguth, P., Kuang, G.: Compressed Sensing for Robust Texture Classification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 383–396. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. PAMI 27, 1265–1278 (2005)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV 73, 213–238 (2007)CrossRefGoogle Scholar
  20. 20.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. IJCV 62, 61–81 (2005)Google Scholar
  21. 21.
    Croiser, M., Griffin, L.D.: Using basic image features for texture classification. IJCV 88, 447–460 (2010)CrossRefGoogle Scholar
  22. 22.
    Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the Significance of Real-World Conditions for Material Classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Xu, Y., Ji, H., Fermuller, C.: View point invariant texture description using fractal analysis. IJCV 83, 85–100 (2009)CrossRefGoogle Scholar
  24. 24.
    Xu, Y., Yang, X., Ling, H., Ji, H.: A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid. In: CVPR (2010)Google Scholar
  25. 25.
    Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: NIPS (1999)Google Scholar
  26. 26.
    Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)Google Scholar
  27. 27.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Feng, X., Pietikinen, M., Hadid, T.: Facial expression recognition with local binary patterns and linear programming. Pattern Recognition and Image Analysis 15, 546–548 (2005)Google Scholar
  29. 29.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. IVC 27, 803–816 (2009)CrossRefGoogle Scholar
  30. 30.
    Vedaldi, A., Zisserman, A.: Efficient additive kernels using explicit feature maps. In: CVPR (2010)Google Scholar
  31. 31.
    Liao, S., Fan, W., Chung, A.C., Yan Yeung, D.: Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features. In: ICIP (2006)Google Scholar
  32. 32.
    Wolf, L., Hassner, T., Taigman, Y.: Similarity Scores Based on Background Samples. In: Zha, H., Taniguchi, R.-I., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 88–97. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Urbach, E.R., Roerdink, J.B., Wilkinson, M.H.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. PAMI 29, 272–285 (2007)CrossRefGoogle Scholar
  34. 34.
    Javier, R.S., Rodrigo, V., Mauricio, C.: Recognition of faces in unconstrained environments: a comparative study. EURASIP Journal on Advances in Signal Processing (2009)Google Scholar
  35. 35.
    Seo, H.J., Milanfar, P.: Face verification using the LARK representation. IEEE Transactions on Information Forensics and Security 6, 1275–1286 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gaurav Sharma
    • 1
    • 2
  • Sibt ul Hussain
    • 1
  • Frédéric Jurie
    • 1
  1. 1.GREYC, CNRS UMR 6072Université de CaenFrance
  2. 2.LEAR, INRIA Grenoble Rhône-AlpesFrance

Personalised recommendations