Computer Science ›› 2016, Vol. 43 ›› Issue (10): 312-316.doi: 10.11896/j.issn.1002-137X.2016.10.058

Previous Articles     Next Articles

Research of Face Recognition Algorithm Based on Nonnegative Tensor Factorization

LIANG Qiu-xia, HE Guang-hui, CHEN Ru-li and CHU Jian-pu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Face recognition is an active research area of biometric identification.Nonnegative tensor factorization is the multiple linear extension of nonnegative matrix factorization,which has been successfully applied to face recognition and other fields.A face recognition algorithm based on nonnegative tensor factorization was proposed.This method need not transform a face matrix into a vector,thereby maintaining the internal structure of the human face matrix,thus maintaining the overall structure of the facial images and making the extraction of facial feature more accurate. The experimental results show that,compared with the classical face recognition algorithms such as PCA and NMF,the face recognition algorithm based on nonnegative matrix factorization provides better representation of face patterns and improves the accurate rate of face recognition.

Key words: Face recognition,Nonnegative matrix factorization,Nonnegative tensor factorization

[1] Belhumeur P,Hespanha J,Kriegman D.Eigenfaces vs.fisherfa-ces:Recognition using class specific linear projection [J].IEEE Trans Pattern Analysis and Machine Intelligence,1997,9(7):711-720
[2] Zhao W Y,Chellappa R,Rosenfeld R,et al.Face recognition a literature survey[J].ACM Computing Survey,2003,35(4):399-458
[3] He Guang-hui,Tang Yuan-yan,Fang Bin,et al.Weightinessimage partition in 3D face recognition[C]∥ International Conference on Systems,Man and Cybernetics.2009:5068-5071
[4] Zhang Tai-ping,Tang Yuan-yan,Fang Bin,et al.Face Recognition under Varying Illumination Using Gradient face[J].IEEE Transactions on Image Processing,2009,18(11):2599-2606
[5] Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(21):788-791
[6] Lee D D,Seung H S.Algorithms for non-negative matrix factori-zation[C]∥Advances in Neural Information Processing.2000
[7] Zhang Tai-ping,Fang Bin,Tang Yuan-yan,et al.Topology preserving non-negative matrix factorization for face recognition[J].IEEE Transactions on Image Processing,2008,17(4):574-584
[8] Yan Shui-cheng,Xu Dong,Yang Qiang,et al.Multilinear dis-criminant analysis for face recognition[J].IEEE Trans on Image Processing,2007,6(1):212-220
[9] Welling M,Weber M.Positive tensor factorization [J].Pattern Recognition Letters,2001,2(12):1255-1261
[10] Kim Y D,Chio S.Nonnegative tucker decomposition [C]∥Proc of IEEE Conference on Computer Vision and Pattern Recognition.2007:1-8
[11] Hazan T,Polak S,Shashua A.Sparse Image Coding Using a 3D Non-Negative Tensor Factorization[C]∥Proceedings of the 10th IEEE International Conference on Computer Vision(ICCV 2005).IEEE Computer Society Press,2005,1:50-57
[12] Liu Ya-nan,Tu Zheng-zheng,Luo Bin.Non-negative TensorFactorization Based on Feedback Space Constraints[J].Journal of Computer Application,2013,3(10):2871-2873(in Chinese) 刘亚男,涂铮铮,罗斌.基于反馈稀疏约束的非负张量分解算法[J].计算机应用,2013,3(10):2871-2873
[13] Shahua A,Lein A.Linear image coding for regression and classification using the tensor-rank principle[C]∥Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition.Hawaii,Dec.2001
[14] Donoho D,Stodden V.When does non-negative matrix factorization give a correct decomposition into parts .http://hdl.handle.net/10022/AC:P:11433
[15] Welling M,Weber M.Positive tensor factorization[J].Pattern Recognition Letters,2001,2(12):1255-1261
[16] Zafeiriou S,Tefas A,Buciu I,et al.Exploiting discriminant information in nonnegative matrix factorization with application tom frontal face verification[J].IEEE Trans.Neural Netw,2006,17(3):683-695

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!