Abstract
Local Binary Patterns (LBP) is one of the efficient approaches for image representation, especially in the face recognition field. The motivation of the present study is to find a compact descriptor which captures texture information and yet is robust against several visual challenges such as illumination variation, facial expressions and head pose variation. The proposed approach, called it Enhance Line Local Binary Patterns (EL-LBP), is an improvement of 1D-Local Binary Patterns (1D-LBP) by reducing the dimension of feature vectors within 1D-LBP histogram and it leads to decrease the time cost during the matching stage. Experiments using ORL, Yale and AR datasets show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.
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Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36
Benzaoui, A., Boukrouche, A., Doghmane, H., Bourouba, H.: Face recognition using 1DLBP, DWT and SVM. In: 2015 3rd International Conference on Control, Engineering Information Technology (CEIT), pp. 1–6, May 2015
Chan, C.-H., Kittler, J., Messer, K.: Multi-scale local binary pattern histograms for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 809–818. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_85
Heikkil, M., Pietikinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)
Houam, L., Hafiane, A., Jennane, R., Boukrouche, A., Lespessailles, E.: Trabecular bone anisotropy characterization using 1D local binary patterns. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010. LNCS, vol. 6474, pp. 105–113. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17688-3_11
Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) 41(6), 765–781 (2011)
Huang, P., Gao, G., Qian, C., Yang, G., Yang, Z.: Fuzzy linear regression discriminant projection for face recognition. IEEE Access 5, 4340–4349 (2017)
Lahdenoja, O., Laiho, M., Paasio, A.: Reducing the feature vector length in local binary pattern based face recognition. In: IEEE International Conference on Image Processing 2005, vol. 2, pp. 914–917, September 2005
Le, D.T., Truong, H.P., Le, T.H.: Facial expression recognition using statistical subspace. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5981–5985, October 2014
Le, T.H., Truong, H.P., Do, H.T.T., Vo, D.M.: On approaching 2D-FPCA technique to improve image representation in frequency domain. In: Proceedings of the Fourth Symposium on Information and Communication Technology, SoICT 2013, pp. 172–180. ACM, New York (2013). https://doi.org/10.1145/2542050.2542061
Martinez, A., Benavente, R.: The AR face database. CVC Technical report, 24 (1998)
Mi, J., Liu, T.: Multi-step linear representation-based classification for face recognition. IET Comput. Vis. 10(8), 836–841 (2016)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Petpon, A., Srisuk, S.: Face recognition with local line binary pattern. In: 2009 Fifth International Conference on Image and Graphics, pp. 533–539, September 2009
Tan, X., Triggs, B.: Fusing gabor and LBP feature sets for kernel-based face recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75690-3_18
Topi, M., Timo, O., Matti, P., Maricor, S.: Robust texture classification by subsets of local binary patterns. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, vol. 3, pp. 935–938, September 2000
Acknowledgement
This work was supported by Institute for information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00731, Personalized Advertisement Platform based on Viewers Attention and Emotion using Deep-Learning Method).
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Truong, H.P., Kim, YG. (2018). Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image Representation for Face Recognition. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_24
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