Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (7): 111-117.doi: 10.11896/jsjkx.190500004

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Face Recognition Based on Cluster Analysis in Complex Environment

GAO Yu-tong1,2, LEI Wei-min1, YUAN Yue2   

  1. 1 School of Computer Science & Engineering,Northeastern University,Shenyang 110169,China
    2 School of Information Engineering,Shenyang University,Shenyang 110044,China
  • Received:2019-05-05 Online:2020-07-15 Published:2020-07-16
  • About author:GAO Yu-tong, born in 1982,Ph.D candidate.Her research interests include image processing and multimedia communication.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61401081)

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Abstract: In modern society,the use of face recognition technology in a variety of fields is increasing.Meanwhile,the problems of social security environment and international security are becoming more serious,thus face recognition is confronted with more severe challenges.Detection target and background are complex and dynamic in a complicated environment,so the traditional face recognition technology can not meet the growing demand.Therefore,in this paper,the traditional SIFT (Scale,Invariant,Feature,Transform) algorithm is optimized by clustering analysis method,and the object features are classified according to the principle of clustering analysis,so as to make the clustering results more in line with the set threshold and improve the matching efficiency.The results show that the improved SIFT algorithm can eliminate the interference of irrelevant books and realize the complete connection of image matching points.In order to verify the effectiveness of the improved SIFT algorithm, it is compared with the common algorithms based on several commonly-used databases,and the results show that the clustering algorithm SIFT is better than other algorithms in CASPEALG R1,CFP,MultiGPIE,and has better application effect and applicability.

Key words: Cluster analysis, Feature matching, SIFT algorithm

CLC Number: 

  • TP391
[1]SHARMA S,KUMAR V.Performance evaluation of 2D face recognition techniques under image processing attacks[J].Mo-dern Physics Letters B,2018,32(19):1850212.
[2]YAN H,WANG P,YANG X L,et al.One Type of New AdaBoost Human Telling Method[J].Journal of Chongqing University of Technology(Natural Science),2019,33(2):106-110,161.
[3]SUN J G,LIU J,ZHAO L Y.Clustering algorithms research[J].Journal of Software,2008,19(1):48-61.
[4]ROSSION B.Humans are visual experts at unfamiliar face reco-gnition[J].Trends in Cognitive Sciences,2018,22(6):471-472.
[5]WANG F F,ZHANG B H,CHAI S C,et al.Community detection in complex networks using proximate support vector clustering[J].Modern Physics Letters B,2018,32(7):1850101.
[6]BAY H,TUYTELAARS T,GOOL L.SURF:speeded up robust features[M]//Computer Vision - ECCV 2006.2006.
[7]REN Y,GU C C.Hand gesture recognition based on HOG characters and SVM[J].Bulletin of Science and Technology,2011,27(2):211-214.
[8]WU Y Z,SUN H H.SIFT feature matching algorithm based on global information[J].Editorial Office of Optics and Precision,2009,17(2):439-444.
[9]YANG Q J,XIAO X M.Improved SIFT algorithm based on Canny feature points[J].Computer Engineering and Design,2011,32(7):2428-2431,2458.
[10]PAN J J,YANG X,XIE X,et al.SIFT:a method for background subtraction in maximum likelihood fit[J].Physics,2009,20(2/3):121-131.
[11]GAO J,HUANG X H,PENG G,et al.Color-based SIFT feature point detection and matching[J].Computer Engineering and Applications,2007,43(34):10-12,15.
[12]MOKHAYERI F,GRANGER E,BILODEAU G A.Domain-specific face synthesis for video face recognition from a single sample per person[J].IEEE Transactions on Information Forensics and Security,2019,14(3):757-772.
[13]HU M,LIU C P,CUI Z M,et al.Clustering difference image kernel density estimation for foreground object detection[J].Journal of Image and Graphics,2009,14(10):2126-2131.
[14]LIU S,LIN K Z,SUN X D,et al.Scale-invariant feature transform based on clustering in face recognition[J].Journal of Harbin University of Science and Technology,2014,19(1):31-35.
[15]YANG X F,LIU P.Face recognition based on clustering optimization algorithm for active model[J].Computer Simulation,2012,29(1):238-241.
[16]HE G H,PAN Y J,WU F.Colorful human face detection based on skin color features and dynamic clustering[J].Opto-Electro-nic Engineering,2004,31(11):47-50.
[17]MA H J,ZHANG D T,FENG J,et al.Three-dimensional facial feature points matching based on K-means clustering of relative angle context distribution and support vector machine[J].Journal of Image and Graphics,2011,16(5):850-856.
[18]ZHOU T,ZHANG Y N,YUAN H J,et al.Sequence targetsclassification algorithm based on ensemble improvement SVM and clustering analysis[J].Computer Science,2009,36(1):148-152.
[19]HO H T,CHELLAPPA R.Pose-invariant face recognition using Markov random fields[J].IEEE Transactions on Image Proces-sing,2013,22(4):1573-1584.
[20]DING C X,TAO D C.Pose-invariant face recognition with homography-based normalization[J].Pattern Recognition,2017,66:144-152.
[21]SU Y,GAO X B,YIN X C.Fast alignment for sparse representation based face recognition[J].Pattern Recognition,2017,68:211-221.
[22]MA H,SUN W C,SHI J H,et al.A Low-Resolution of Face Recognition Method Based on Curvelet Transform[J].Journal of Chongqing University of Technology(Natural Science),2018,32(11):162-168.
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