Abstract
In this paper, a low rank representation based projections (LRRP) method is presented for face recognition. In LRRP, low rank representation is used to construct a nuclear graph to characterize the local compactness information by designing the local scatter matrix like SPP; the total separability information is characterized by the total scatter like PCA. LRRP seeks the projection matrix simultaneously maximizing the total separability and the local compactness. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that LRRP works well for face recognition.
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References
Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–459
Bowyer KW, Chang K, Flym P (2006) A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput Vis Image Underst 101(1):1–15
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York
Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252–264
Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836
Belhumeur V, Hespanha J, Kriegman D (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Yang J, Yang J (2003) Why can LDA be performed in PCA transformed space? Pattern Recognit 36(2):563–566
Hong Z, Yang J (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit 24(4):317–324
Yang J, Li H (2010) PCA based sequential feature space learning for gene selection. In: ICMLC2010, pp 3079–3084
Hastie T, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 23:73–102
Chen LF, Liao HYM, Ko MT, Yu GJ (2000) A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognit 33(1):1713–1726
Yu H, Yang J (2001) A direct LDA algorithm for high dimensional data—with application to face recognition. Pattern Recognit 34(10):2067–2070
Pang Y, Tao D, Yuan Y, Li X (2008) Binary two-dimensional PCA. IEEE Trans Syst Man Cybern B Cybern 38(4):1176–1180
Zhuang X, Dai D (2005) Inverse fisher discriminant criteria for small sample size problem and its application to face recognition. Pattern Recognit 38(11):2192–2194
Jin Z, Yang J, Hu Z, Lou Z (2001) Face recognition based on the uncorrelated discrimination transformation. Pattern Recognit 34(7):1405–1416
Li X, Lin S, Yan S, Xu D (2008) Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst Man Cybern B Cybern 38(2):342–352
Loog M (2001) Approximate pairwise accuracy criterion for multiclass linear dimension reduction: generalization of the fisher criterion, IEEE Trans. Pattern Anal Mach Intell 26(7):762–766
Xu J, Yang J (2013) K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis. Inf Sci 232(2):11–26
Lu G, Wang Y (2012) Feature extraction using a fast null space based linear discriminant analysis algorithm. Inf Sci 193(15):72–80
Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2):260–274
Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262
Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on Laplacian bidirectional maximum margin criterion. Pattern Recognit 42(11):2327–2334
Yang W, Wang J, Ren M, Zhang L, Yang J (2009) Feature extraction using fuzzy inverse FDA. Neurocomputing 72(13–15):3384–3390
Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323
Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290:2323–2326
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
Donoho David L, Grimes Carrie (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci 100(10):5591–5596
Liu W, Tao D (2013) Multiview Hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676–2687
He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Yang J, Zhang D, Yang J, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664
Xu Y, Zhong A, Yang J, Zhang D (2010) LPP solution schemes for use with face recognition. Pattern Recognit 43(12):4165–4176
Yan S, Xu D, Zhang B, Zhang H (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51
Wang H, Che S, Hu Z, Zheng W (2008) Locaility-preserved maximum information projection. IEEE Trans Neural Netw 19(4):571–585
Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Proceedings on neural information processing systems, Vancouver, BC, Canada, pp 1601–1608
Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272
Yu J, Rui R, Tang Y, Tao D (2014) High-order distance based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442
Yu J, Rui R, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032
Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw patches. In: CVPR2008
Rao S, Tron R, Vidal R, Ma Y (2008) Motion segmentation via robust subspace separation in the presence of outlying, incomplete, and corrupted trajectories. In: CVPR2008
Mairal J, Sapiro G, Elad M (2008) Learning multiscale sparse representations for image and video restoration. SIAM MMS 7(1):214–241
Liu W, Tao D, Cheng J, Tang Y (2014) Multiview Hessian discriminative sparse coding for image annotation. Comput Vision Image Underst 118:50–60
Liu W, Song C, Wang Y (2012) Facial expression recognition based on discriminative dictionary learning. In: ICPR2012
Wang H, Yuan C, Hu W et al (2014) Action recognition using nonnegative action component representation and sparse basis selection. IEEE Trans Image Process 23(2):570–581
Yu J, Hong R, Wang M, You J (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47:3512–3519
Wright J, Yang A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227
Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognit 43(1):331–341
Cheng B, Yang J, Yan S, Fu Y, Huang T (2010) Learning with L1-graph for image analysis. IEEE Trans Image Process 19(4):858–866
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition. In: ICCV2011
Liu G, Lin Z, Yan S, Su J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184
Zhang N, Yang J (2013) Low-rank representation based discriminative projection for robust feature extraction. Neurocomputing 111:13–20
Lin Z, Chen M, Wu L, Ma Y (2009) The argumented lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC technical report UIUC-ENG-0902215, Tech. Rep
Zhao D, Lin Z, Tang X (2007) Laplacian PCA and its applications. In: ICCV2007
He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: ICCV2005, pp 1208–1213
Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104
Phillips PJ (2004) The facial recognition technology (FERET) database, http://www.itl.nist.gov/iad/humanid/feret/feret_master.html
Martinez AM, Benavente R (1998) The AR face database http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html
Martinez AM, Benavente R (1998) The AR face database. CVC technical report #24, June 1998
Zhang L, Zhang L, Zhang D, Zhu H (2010) Online finger-knuckle-print verification for personal authentication. Pattern Recognit 43(7):2560–2571
The Hong Kong PolyU FKP database. http://www4.comp.polyu.edu.hk/~biometrics/FKP.htm
Acknowledgments
This project is partly supported by NSF of China (61375001), partly supported by the open fund of Key Laboratory of Measurement and partly supported by Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2013B01), and the Open Project Program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04). Understanding for Social Safety (Nanjing University of Science and Technology), (No. 30920130122006).
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Wang, Z., Yang, W. & Shen, F. Face Recognition Using A Low Rank Representation Based Projections Method. Neural Process Lett 43, 823–835 (2016). https://doi.org/10.1007/s11063-015-9448-z
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DOI: https://doi.org/10.1007/s11063-015-9448-z