Abstract
In this paper, we propose a novel representation, called Multi-scale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.
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Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision, Prague, Czech, pp. 469–481 (2004)
Balas, B., Sinha, P.: Toward dissociated dipoles: Image representation via non-local comparisons. In: CBCL Paper #229/AI Memo #2003-018, MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA (August 2003)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Crow, F.: Summed-area tables for texture mapping. SIGGRAPH 18(3), 207–212 (1984)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity (July 1998)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)
Moghaddam, B., Nastar, C., Pentland, A.: A Bayesain similarity measure for direct image matching. Media Lab Tech Report No.393, MIT (August 1996)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Ojala, T., Pietikainen, M., Maenpaa, M.: 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)
Penev, P., Atick, J.: Local feature analysis: A general statistical theory for object representation. Neural Systems 7(3), 477–500 (1996)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2005)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91 (1998)
Simard, P.Y., Bottou, L., Haffner, P., Cun, Y.L.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 571–577. MIT Press, Cambridge (1998)
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Viola, P., Jones, M.: Robust real time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, July 13, 2001 (2001)
Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.v.d.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 180–187. Springer, Heidelberg (2004)
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Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z. (2007). Learning Multi-scale Block Local Binary Patterns for Face Recognition. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_87
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DOI: https://doi.org/10.1007/978-3-540-74549-5_87
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