CCBR 2014: Biometric Recognition pp 111-119 | Cite as
Research of Improved Algorithm Based on LBP for Face Recognition
Abstract
Face recognition is one of the research hotspots in the area of computer vision and pattern recognition which has a wide application perspective. In this paper, a research on the classical Local Binary Pattern (LBP) is made and an improved algorithm named double-circle LBP is proposed, which can further enhance the rotation invariant characteristic of LBP. Since LBP descriptor based on the block has good recognition effect, this paper further proposed the strategy of "multiple blocks+middle block" in double-circle LBP descriptor, which can effectively solve the problem that the information around the original block line cannot be extracted completely. Finally, experiments are conducted on Orl, Yale and Extended YaleB face databaseds by comparing the recognition rate by using original LBP and its improved algorithms. The results show that double-circle LBP descriptor and "multiple -block+middle-block LBP descriptor can greatly improve the recognition rate.
Keywords
face recognition LBP double-circle LBP multiple blocks+middle block strategyPreview
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References
- 1.Heikkila, M., Pietikainen, M., Heikklia, J.: A texture — based method for modeling the background and detecting moving objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)CrossRefGoogle Scholar
- 2.Ojala, T., Pietikäinen, 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)CrossRefGoogle Scholar
- 3.Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49(2), 117–125 (2012)CrossRefGoogle Scholar
- 4.Pietikäinen, M.: Image analysis with local binary patterns[M]//Image Analysis, pp. 115–118. Springer, Heidelberg (2005)Google Scholar
- 5.Ahonen, T., Pietikainen, M.: A Framework for analyzing texture descriptors. In: VISAPP 2008: Third International Conference on Computer Vision Theory and Applications, pp. 507–512. Springer, Heidelberg (2008)Google Scholar
- 6.PietikÄainen, M., Hadid, A., Zhao, G.Y., Ahonen, T.: Computer Vision Using Local Binary Patterns, pp. 193–202. Springer, Heidelberg (2011)Google Scholar
- 7.Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)CrossRefGoogle Scholar
- 8.Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Systems with Applications 39(3), 3634–3641 (2012)CrossRefGoogle Scholar
- 9.Ahonen, T., Pietikäinen, 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
- 10.Zhang, W., Shan, S., Zhang, H., Gao, W., Chen, X.: ulti-resolution histograms of local variation patterns (MHLVP) for robust face recognition. In: Proc. Audio- Video-Based Biometric Person Authent., pp. 937–944 (2005)Google Scholar
- 11.Zhang, L., Chu, R., Xiang, S., Li, S.Z.: Face detection based onMulti-Block LBP representation. In: Proc. Int. Conf. Biometrics, pp. 11–18 (2007)Google Scholar
- 12.Wolf, L., Hassner, T., Taigman, Y.: Descriptor Based Methods in the Wild. In: Workshop in ECCV (2008)Google Scholar
- 13.Zhao, G., Pietikäinen, M.: Dynamic texture recognition using volume local binary patterns dynamical vision. In: Proceedings of European Conference on Computer Vision Workshop on Dynamical Vision, Graz, Austria, pp. 165–177 (2006)Google Scholar
- 14.Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 915–928 (2007)CrossRefGoogle Scholar
- 15.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)CrossRefGoogle Scholar
- 16.Zhang, W.C., Shan, S.G., Gao, W., Chen, X.L., Zhang, H.M.: Local gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th International Conference on Computer Vision, pp. 786–791. IEEE, Beijing (2011)Google Scholar