Abstract
In this paper, we propose a novel approach to face recognition, called Multi-scale Block Local Ternary Patterns (MB-LTP), which considers both local and various scale texture information to represent face images. In MB-LTP, we compare average values of sub-regions and use a 3-valued codes method to get the MB-LTP value. The MB-LTP histograms are then extracted and concatenated into a single, spatially enhanced feature vector representing the face image in recognition. We use a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. MB-LTP code presents several advantages: (1)It is more robust than LBP;(2)it is more discriminative and less sensitive to noise;(3)it encodes not only microstructures but also macrostructures of image patterns. Experiments on ORL and AR databases show that the proposed MB-LTP method significantly outperforms other LBP based face recognition algorithms.
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References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Guodail, F., Lange, E., Iwamoto, T.: Face recognition system using local autocorrelations and multiscale integration. IEEE TPAMI 18(10), 1024–1028 (1996)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE TPAMI 27(5), 684–698 (2005)
Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE TPAMI 28(5), 725–737 (2006)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 971–987 (2002)
Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning Multi-scale Block Local Binary Patterns for Face Recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
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Zhu, L., Zhang, Y., Sun, C., Yang, W. (2013). Face Recognition with Multi-scale Block Local Ternary Patterns. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_27
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DOI: https://doi.org/10.1007/978-3-642-36669-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
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