计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 208-215.doi: 10.11896/jsjkx.200800155
陈长伟1,2, 周晓峰1
CHEN Chang-wei1,2, ZHOU Xiao-feng1
摘要: 针对协同表示分类器(CRC)计算时间复杂度较高的问题,利用重构系数的大小与样本标签之间的正相关性,提出了局部快速协同表示器并用于人脸识别。首先使用最小二乘法求解L2范数约束下的线性回归问题;然后对重构系数进行筛选,舍弃对分类不利的负重构系数;最后抛弃原CRC算法中的样本重构环节,转而使用最大相似性准则确定测试样本所属分类。该方法利用样本的局部相似性,使识别率得到了一定的提升。同时该方法无需样本重构,求解复杂度大幅度降低。在AR和CMU PIE数据集上的实验结果表明,所提方法的时间复杂度极大幅度优于CRC,且在各种光照、表情、角度等状态下其识别率均高于现有其他相关算法。
中图分类号:
[1]JAYARAMAN U,GUPTA P,GUPTA S,et al.Recent Deve-lopment in Face Recognition[J].Neurocomputing,2020,408:231-245. [2]CHENG E J,CHOU K P,RAJORA S T,et al.Deep Sparse Representation Classifier for Facial Recognition and Detection System[J].Pattern Recognition Letters,2019,125:71-77. [3]MOKHAYERI F,GRANGER E.A paired sparse representation model for robust face recognition from a single sample[J/OL].http://arXiv.org/abs/1910.02192. [4]COVER T,HART P.Nearest Neighbor Pattern Classification[J].IEEE Transactions on Information Theory,2003,13(1):21-27. [5]GONZALEZ R C,WOODS R E.Digital Image Processing[M].Addison Wesley,1997. [6]DUDA R O,HART P E,STORK D G.Pattern Classification(2nd Edn.)[M].Wiley,2000. [7]MITANI Y,HAMAMOTO Y.A Local Mean based Nonparametric Classifier[J].Pattern Recognition Letters,2006,27(10):1151-1159. [8]WRIGHT J,YANG A Y,GANESH A,et al.Robust Face Re-cognition via Sparse Representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(2):210-227. [9]SONG X N,HU G S,LUO J H,et al.Fast SRC using Quadratic Optimisation in Downsized Coefficient Solution Subspace[J].Signal Processing,2019,161:101-110. [10]NASEEM I,TOGNERI R,BENNAMOUN M.Linear Regres-sion for Face Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(11):2106-2112. [11]ZHANG L,YANG M,FENG X C.Sparse Representation orCollaborative Representation:Which helps Face Recognition? [C]//International Conference on Computer Vision.2012. [12]YANG A Y,ZHOU Z,GANESH A,et al.Fast L1-Minimization Algorithms for Robust Face Recognition[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Proces-sing Society,2010,22(8):3234-3246. [13]BERNHARD S,JOHN P,THOMAS H.Sparse Representation for Signal Classification[C]//International Conference on Neural Information Processing Systems.2016. [14]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227. [15]GAO S H,TSANGI W H,CHIA L T.Kernel Sparse Representation for Image Classification and Face Recognition[C]//European Conference on Computer Vision.2010. [16]WANG B,LI W F,POH N,et al.Kernel collaborative representation-based classifier for face recognition [C]//IEEE International Conference on Acoustics,Speech and Signal Processing.2013:2877-2881. [17]WANG D,LU H,YANG M.Kernel collaborative face recognition[J].Pattern Recognition,2015,48(10):3025-3037. [18]VO D M,LEE S W.Robust face recognition via hierarchical collaborative representation[J].Information Sciences,2017,432:332-346. [19]DONOHO D.For Most Large Underdetermined Systems of Li-near Equations the Minimal L1-Norm Solution is also the Sparsest Solution[J].Communications on Pure and Applied Mathematics,2006,59(6):797-829. [20]TROPP J A,WRIGHT S J.Computational methods for sparse solution of linear inverse problems[C]//Proceedings of IEEE,Special Issue on Applications of Compressive Sensing & Sparse Representation.2010:948-958. [21]XU J,YANG J.Mean Representation based Classifier with its Applications[J].Electronics Letters,2011,47(18):1024-1026. [22]MARTINEZ A M,BENAVENTE R.The AR face database[R].CVC Technical Report #24,1998. [23]GEORGHIADES A,BELHUMEUR P,KRIEGMAN D.From Few to Many:Illumination Cone Models for Face Recognition under Variable Lighting and Pose[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):643-660. [24]LEE K,HO J,KRIEGMAN D.Acquiring Linear Subspaces for Face Recognition under Variable Lighting[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):684-698. [25]JUAN L,MARTÍNEZ F,ZULIMA F.The curse of dimensiona-lity in inverse problems[J/OL].Journal of Computational and Applied Mathematics,2020,https://doi.org/10.1016/j.cam.2019.112571. [26]TURK M,PENTLAND A.Eigenfaces for recognition[J].JCogn Neurosci,1991,3(1):71-86. [27]BLEI D M,NG A Y,JORDAN M I.Latent Dirichlet Allocation[J].Machine Learning Research Archive,2003,3:993-1022. [28]HE X F.Locality Preserving Projections[J].Advances in Neural Information Processing Systems,2003,16(1):186-197. [29]HUANG P,YANG Z,CHEN C.Fuzzy Local Discriminant Embedding for Image Feature Extraction[J].Comput. Electr. Eng.,2015,46:231-240. [30]HUANG L,MA Y Q,LIU X L.A General Non-Parametric Active Learning Framework for Classification on Multiple Manifolds[J].Pattern Recognition Letters,2020,130:250-258. [31]MENG Y,SHANG R,JIAO L,et al.Feature Selection BasedDual-graph Sparse Non-negative Matrix Factorization for Local Discriminative Clustering[J].Neurocomputing,2018,290(17):87-99. [32]GROSS R,MATTHEWS I,COHN J,et al.Multi-PIE[J].Image and Vision Computing,2010,28(5):807-813. [33]PHILLIPS P J,WECHSLER H,HUANG J S,et al.The FERET Database and Evaluation Procedure for Face-Recognition Algorithms[J].Image and Vision Computing,1998,16(5):295-306. |
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