IJIPVS

A novel approach for Face Recognition using Local Binary Pattern A novel approach for Face Recognition using Local Binary Pattern

---

---

Abstract

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible.

Recommended Citation

[1] Yu Wang, Xueye Wei, Shuo Xiao, “LBP texture analysis Based on the Local Adaptive Niblack Algorithm,” 2008 Congress on Image and Signal Processing, DOI 10.1109/CISP.2008.403 [2] Zhenhua Guo,LeiZhang, DavidZhang, “Rotation invariant texture classification using LBP variance(LBPV) with global matching,” Pattern Recognition, 43 (2010) pp.706–719[3] Hui Zhou , Runsheng Wang, Cheng Wang, “A novel extended local binary-pattern operator for texture analysis,” Information Sciences 178 (2008), pp.4314–4325 [4] Guoying Zhao and Matti Pietik¨ainen, “Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions,” IEEE transactions on Pattern Analysis and machine intelligence, 2007 [5] Matti Pietikanen, “Image analysis with Local binary Patterns,” Lecture notes in Computer Science, 2005, volume 3540/2005. [6] Jun Meng, Yumao Gao, Xiukun Wang, Tsauyoung Lin, Jianying Zhang, “Face Recognition based on Local Binary Patterns with Threshold,” 2010 IEEE International Conference on Granular Computing, DOI 10.1109/GrC.2010.72 [7] Xiaoshan Liu, Minghui Du, Lianwen Jin, “Face Features Extraction Based on multi-scale LBP,” 2010 2nd International Conference on Signal Processing Systems (ICSPS) [8] Hengliang Tang, Yanfeng Sun, Baocai Yin, Yun Ge, “Expression Robust 3d Face Recognition Using LBP Representation,” 2010 IEEE [9] Wencheng Wang, Faliang Chang , Jianguo Zhao, Zhenxue Chen, “Automatic Facial Expression Recognition Using Local Binary Pattern,” Proceedings of the 8th World Congress on Intelligent Control and Automation July 6-9 2010 [10] Jae Young Choi, Plataniotis, K.N, Yong Man Ro, “Using colour Local Binary Pattern for Face Recognition,” Image Processing(ICIP), 2010 17th International Conference on Digital Object Identifier, pp.4541 4544 [11] Mihran Tuceryan, Anil K. Jain, “Texture Analysis, The Handbook of