Face Recognition Based on Improved LBP and LS-SVM


Article Preview

In order to extract characteristics of face by making full use of LBP and improve its "adaptive ability", we proposed an algorithm based on global and local fusion LBP. First, we will extract overall face feature histogram with LBP, then segment the image into blocks, extract each LBP histogram feature, then combine the global and local features according to certain order , and we regard it as the total character of image. Finally, we use the LS- SVM (least squares support vector machine) identifying and training samples of face image to improve the speed of recognition the experiment on ORL face database shows that the algorithm has high recognition rate and has improved recognition rate of the face.



Advanced Materials Research (Volumes 403-408)

Edited by:

Li Yuan




J. G. Sun et al., "Face Recognition Based on Improved LBP and LS-SVM", Advanced Materials Research, Vols. 403-408, pp. 3249-3252, 2012

Online since:

November 2011




[1] Timo A, Abdenour H, Matti P. Face recognition with local binary patterns. In: Proc. of the European conf. onComputer Vision. Springer-Verlag, 2004. 469−481.

[2] BIAN Zhao·qi, ZHANG Xue·gong, YAN Ping-fan, et a1. Pattern Recognition[M]. 2th ed. Bering; Tsinghtla UniversityPress. (2000).

[3] OJALA T, PIETIKAINEN M, HARWOOD D. A comparative study of texture measures with classification basedon feature distributions[J]. Pattern Recognition, 1996. 51-59.

DOI: 10.1016/0031-3203(95)00067-4

[4] OJALA T, PietikanenM, MaenpaaT. Multiresoulution gray2scale and rota2tion invariant texture classification with lacal binary patterns[J]. IEEETrans on PAMI, 2002, 24: 971-987.

[5] Vapnik V. N. The Nature of Dtatistical Learning Theory[M]. NewYork : Springer- Verlag , 1995: 235- 313.

Fetching data from Crossref.
This may take some time to load.