Computer Science ›› 2020, Vol. 47 ›› Issue (6): 121-125.doi: 10.11896/jsjkx.190500058

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine

WU Qing-hong, GAO Xiao-dong   

  1. School of Electronic and Information Engineering,University of Science and Technology Liaoning,Anshan,Liaoning 114051,China
  • Received:2019-05-14 Online:2020-06-15 Published:2020-06-10
  • About author:WU Qing-hong,born in 1967,Ph.D,professor.His main research interests include pattern recognition,network communication and automation.
  • Supported by:
    This work was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAF05B00).

Abstract: Currently face recognition algorithms have high recognition accuracy and strong adaptive ability in ideal environment,but in non-ideal environment,the accuracy of face recognition declines sharply.In order to improve the stability of face recognition results,a non-ideal environment face recognition algorithm based on sparse representation and support vector machine fusion is designed.Firstly,the feature dictionary of face recognition in non-ideal environment is constructed,then the training samples and test samples of face recognition in non-ideal environment are processed by feature dictionary,and the learning samples of facere-cognition in non-ideal environment are constructed.Finally,the classifier of face recognition in non-ideal environment is established by using support vector machine,and face recognition in non-ideal environment is processed.A number of standard face databases are used to test the non-ideal environment face recognition algorithm.The non-ideal environment face recognition accuracy of this algorithm is high,the false recognition rate and rejection rate of non-ideal environment face recognition are low.Compared with other face recognition algorithms,it is more adaptable to environmental changes,and the overall recognition effect of non-ideal environment face is better.It improves the efficiency of face recognition in non-ideal environment and has obvious advantages.

Key words: Face recognition, Illumination change, Imperfect environment, Robustness, Sparse representation

CLC Number: 

  • TP311
[1]WEI D M,ZHANG L R,HU N N,et al.Hyperspectral face recognition algorithm combining spatial spectrum information and Gabor features [J].Journal of Beijing University of Technology,2017,37(10):1077-1083.
[2]CUI Y F,LI K Y,HU Y,et al.Face Recognition by Joint Discriminant Low Rank Classification Dictionary and Sparse Error Dictionary Learning [J].Chinese Journal of Image Graphics,2017,22(9):1222-1229.
[3]WU D,HU H,LI Y.Robust Face Recognition Based on Significance Difference Local Orientation Model and Deep Convolution Network [J].Optoelectronic Laser,2017,28(8):902-909.
[4]YI T T,DONG C X.Threedimensional face recognition method based on facial expression GEM and sparse cubic matrix [J].Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2017,2(2):257-264.
[5]SHOU Z Y,YANG X F.Face recognition algorithm combined with Gabor error dictionary and low rank representation [J].Computer Science,2017,44(3):296-299.
[6]CHENG S H,LIU J.Face recognition under complex illumination based on multi-scale Weber face and gradient face [J].Journal of Metrology,2017,38(1):60-64.
[7]YANG F F,WU X S,GU B Z.Sparse Representation Face Recognition Algorithms Based on Low Rank Subspace Projection and Gabor Features [J].Computer Engineering and Science,2017,39(1):131-137.
[8]HU Z P,BAI F,WANG M,et al.Regular robust sparse representation face recognition algorithm with supervised low rank subspace restoration [J].Signal Processing,2016,32(11):1299-1307.
[9]HE M,DA F P,DENG X.3-D face recognition under partial occlusion of radial line [J].Chinese Journal of Image and Graphi-cs,2018,23(8):1163-1170.
[10]HUO Y H,FAN W Q.A Face Recognition Method under Complex Illumination Conditions in Coal Mines [J].Advances in Laser and Photoelectronics,2019,56(1):116-123.
[11]XUE S,ZHU H,WANG J,et al.Iterative Label Propagation Recognition Algorithms for Low Resolution Face Images [J].Pattern Recognition and Artificial Intelligence,2018,31(7):602-611.
[12]LI X X,LI J J,HE L,et al.Robust Face Recognition Method Based on Noise Spatial Structure Embedding and High Dimensional Gradient Direction Embedding [J].Computer Science,2018,45(4):285-290.
[13]ZHAO Y L,YUAN Q D,MENG X P.Multi-pose face recognition algorithm based on sparse coding and machine learning [J].Journal of Jilin University(Science Edition),2018,56(2):340-346.
[14]LI Y L,LIU Y C,XIAO Z T,et al.Fuzzy Face Image Identification Based on High Frequency Analysis of Key Marker Points [J].Minicomputer System,2018,39(2):386-392.
[15]ZHANG D F,GAO N H,WANG R,et al.Face recognition algorithm based on block LBP fusion feature and SVM [J].Sensors and Microsystems,2019(5):154-156,160.
[16]QIU Y M,LIAO H B,CHEN Q H.Face pose recognition based on Discriminant dictionary learning [J].Journal of Wuhan University(Information Science Edition),2018,43(2):275-281,288.
[17]WANG Y,SHEN X J,CHEN H P.Video Face Recognition Based on Modified Fisher Criteria and Multi-instance Learning [J].Acta Automatica Sinica,2018,44(12):2179-2187.
[18]MOU Q,WEI Y Y,LI J,et al.Research on improved Retinex low illumination image enhancement algorithm [J].Journal of Harbin University of Engineering,2018,39(12):2001-2010.
[19]LI Y,ZHANG Y.Illumination robust face recognition method based on stochastic projection and weighted sparse representation of residual [J].Computer Engineering and Science,2018,40(11):2015-2022.
[20]REN S B,LIAO X D.Software defect prediction based on cost-sensitive support vector machine [J].Computer Engineering and Science,2018,40(10):1787-1795.
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