Abstract
Face recognition has recently become very hot research topic in computer vision and multimedia information processing. To integrate the human perception of local micro-pattern to face recognition, this paper proposes an improve LBP face representation based on Weber’s law. First, inspired by psychological Weber’s law, the human perception of local micro-pattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors and the other is intensity of local central pixel. Second, regarding the perception of local micro-pattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and uniform patterns are applied to get final features. Three face image databases, namely, ORL, Yale and Extended Yale-B, are used to evaluate performance. Experimental results demonstrate the effectiveness and superiority of our proposed face recognition method.
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References
Choi, J., Yang, S., Ro, Y., Plataniotis, K.: Face annotation for personal pholts using context-assisted face recognition. ACM MIR, 44–51 (2008)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Ruiz-del-Solar, J., Verschae, R., Correa, M.: Recognition of faces in unconstrained environments: A comparative study. EURASIP Journal on Advances in Signal Processing, 1–20 (2009)
Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Transaction Pattern Analysis and Machine Intelligence 26(9), 1222–1228 (2004)
Li, S.Z., Chu, R., Liao, S.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Transaction on Pattern Analysis and Machine Intelligence 29(4), 627–639 (2007)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transaction Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Ojala, T., Pietikäinen, M.: Multi-resolution, Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Zhang, B., Gao, Y., Zhao, S., et al.: Local Derivative Pattern versus Local Binary Pattern: Face recognition with High-order Local Pattern Descriptor. IEEE Transaction on Image Processing 19(2), 533–544 (2010)
Xie, S.F., Shan, S., Chen, X., et al.: Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition. IEEE Transaction on Image Processing 19(5), 1349–1361 (2010)
Jabid, T., Kabir, H., Chae, O.: Gender Classification using Local Directional Pattern(LDP). In: 2010 International Conference on Pattern Recognition (ICPR 2010), pp. 2162–2164 (2010)
Liao, S., Chung, A.C.S.: Face Recognition with Salient Local Gradient Orientation Binary Pattern. In: 2009 International Conference on Image Processing (ICIP 2009), pp. 3317–3320 (2009)
Zheng, Y., Shen, C., Hartley, R., Huang, X.: Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 281–292. Springer, Heidelberg (2011), arXiv:1009.0892v2
Tan, X., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. IEEE Transactions on Image Processing, 19(6), pp.1635–1650 (2010)
Zhao, G., Pietikäinen, M.: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence 29(4), 915–928 (2007)
Zhao, G., Pietikäinen, M.: Local Binary Pattern Descriptors for Dynamic Texture Recognition. In: 2006 International Conference on Pattern Recognition (ICPR 2006), pp. 211–214 (2006)
Gupta, R., Patil, H., Mittal, A.: Robust Order-based Methods for Feature Description. In: 2010 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 334–341 (2010)
Chen, J., Shan, S., He, C., et al.: WLD: A Robust Local Image Descriptor. IEEE Transaction on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)
Zhang, B., Shan, S., Gao, W.: Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition. IEEE Transaction on Image Processing 16(1), 57–68 (2007)
Jiang, X.D., Mandal, B., Kot, A.: Eigenfeature Regularization and Extraction in Face Recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 30(3), 383–394 (2008)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall (1989)
Fu, X., Wei, W.: Facial Expression Recognition based on Advance Local Binary Pattern Histogram Projection. Pattern Recognition and Artificial Intelligence 22(1), 123–128 (2009)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J.: Face Recognition Using Laplacianfaces. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Lei, Z., Liao, S., Pietikainen, M., Li, S.Z.: Face Recognition by Exploring Information Jointly in Space, Scale and Orientation. IEEE Transaction on Image Processing 20(1), 247–256 (2011)
Kao, W., Hsu, M., Yang, Y.: Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition. Pattern Recognition 43(5), 1736–1747 (2010)
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Xie, Z., Liu, G., Fang, Z. (2012). Face Recognition Based on Combination of Human Perception and Local Binary Pattern. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_47
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DOI: https://doi.org/10.1007/978-3-642-31919-8_47
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