Face recognition using near infrared images based on LBP and Gabor hybrid feature

  • ZHAO Ji ,
  • TONG Wei-qing
Expand
  • Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China

Received date: 2015-06-12

  Online published: 2016-09-29

Abstract

To take advantages of local binary patterns (LBP) and Gabor, we proposed an algorithm for face recognition based on hybrid features, using cosine similarity and principal component analysis (PCA). Four kinds of LBP and Gabor hybrid feature methods were studied and experimental results on 871 people’s nearinfrared face illustrated that the proposed method attained higher correct rate and lower false accept rate (FAR).

Cite this article

ZHAO Ji , TONG Wei-qing . Face recognition using near infrared images based on LBP and Gabor hybrid feature[J]. Journal of East China Normal University(Natural Science), 2016 , 2016(4) : 77 -85 . DOI: 10.3969/j.issn.1000-5641.2016.04.009

References

[1]YANG P, SHAN S G, GAO W, et al. Face recognition using ada-boosted gabor features[C]//Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. IEEE, 2004: 356-361.
[2]OSUNA E, FREUND R, GIROSI F. Training support vector machines: An application to face detection[C]//Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 1997: 130-136.
[3]SU Y, SHAN S, CHEN X, et al. Hierarchical ensemble of global and local classifiers for face recognition[J]. IEEE Transactions on Image Processing, 2009, 18(8): 1885-1896. 
[4]ZHANG W C, SHAN S G, GAO W, et al. Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition[C]//Proceedings of the 10th IEEE International Conference on Computer Vision. IEEE, 2005(1):786-791. 
[5]SHAN S G, GAO W, CHANG Y Z, et al. Review the strength of gabor features for face recognition from the angle of its robustness to mis-alignment[C]//Proceedings of the 17th International Conference on Pattern Recognition. IEEE, 2004(1): 338-341.
[6]OJALA T, PIETIK INEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1): 51-59.
[7]AHONEN T, HADID A, PIETIKINEN M. Face recognition with local binary patterns[C]//Computer Vision ECCV 2004. Berlin:Springer, 2004: 469-481.
[8]JOLLIFFE I T. Principal Component Analysis[M]. Berlin:Springer, 2002.
[9]OJALA T, PIETIKAINEN M, MAENPAAT. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[10]ZHAO L H, SONG Y, ZHU Y S, et al. Face recognition based on multiclass SVM[C]//Proceedings of the 21st Annual International Conference on Chinese Control and Decision Conference. IEEE, 2009: 5901-2903.
[11]PHILLIPS P J. Support vector machines applied to face recognition[J]. Advances in Neural Information Processing Systems, 2001, 11(7): 803-809.
[12]TAN Y, WANG J. A support vector machine with a hybrid kernel and minimal Vapnik Chervonenkis dimension[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(4): 385-395.
[13] GUO G D, LI S Z, CHAN K. Face recognition by support vector machines[C]//Proceedings of the 4th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. IEEE, 2000: 196-201.

Outlines

/