Double linear regressions for single labeled image per person face recognition

Zhao, 2003, Face recognition: a literature survey, ACM Computing Surveys, 35, 399, 10.1145/954339.954342 Baldi, 2001 Manning, 2008 Bishop, 2006 Jimenez, 1997, Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data, IEEE Transactions on Systems, Man and Cybernetics, 28, 39, 10.1109/5326.661089 Zhou, 2011, Manifold elastic net: a unified framework for sparse dimension reduction, Data Mining and Knowledge Discovery, 22, 340, 10.1007/s10618-010-0182-x Gunal, 2008, Subspace based feature selection for pattern recognition, Information Sciences, 178, 3716, 10.1016/j.ins.2008.06.001 Lu, 2011, A survey of multilinear subspace learning for tensor data, Pattern Recognition, 44, 1540, 10.1016/j.patcog.2011.01.004 D. Zhang, Z. Zhou, S. Chen, Semi-supervised dimensionality reduction, in: Proceedings of the 7th SIAM International Conference on Data Mining (SDM), 2007, pp. 629–634. Zhang, 2012, Graph optimization for dimensionality reduction with sparsity constraints, Pattern Recognition, 45, 1205, 10.1016/j.patcog.2011.08.015 Wright, 2010, Sparse representation for computer vision and pattern recognition, Proceedings of the IEEE, 98, 1031, 10.1109/JPROC.2010.2044470 Cheng, 2013, Sparse representation and learning in visual recognition: theory and applications, Signal Processing, 93, 1408, 10.1016/j.sigpro.2012.09.011 Jolliffe, 1986 Fukunaga, 1990 Turk, 1991, Eigenfaces for recognition, Journal of Congnitive Neuroscience, 3, 71, 10.1162/jocn.1991.3.1.71 Belhumeur, 1997, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 711, 10.1109/34.598228 X. He, P. Niyogi, Locality preserving projections, in: Proceedings of the Advances in Neural Information Processing Systems (NIPS), 2003, pp. 585–591. X. He, D. Cai, S. Yan, H. Zhang, Neighborhood preserving embedding, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2005, pp. 1208–1213. He, 2005, Face recognition using laplacianfaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 328, 10.1109/TPAMI.2005.55 Qiao, 2010, Sparsity preserving projections with applications to face recognition, Pattern Recognition, 43, 331, 10.1016/j.patcog.2009.05.005 Qiao, 2010, Sparsity preserving discriminant analysis for single training image face recognition, Pattern Recognition Letters, 31, 422, 10.1016/j.patrec.2009.11.005 F. Yin, L.C. Jiao, F. Shang, S. Wang, B. Hou, Fast fisher sparsity preserving projections, Neural Computing and Applications 23 (3–4) (2013) 691–705. Aronszajn, 1950, Theory of reproducing kernels, Transactions of the American Mathematical Society, 68, 337, 10.1090/S0002-9947-1950-0051437-7 B. Scholkopf, A.J. Smola, K. Muller, Kernel principal component analysis, in: Proceedings of Advances in Kernel Methods-Support Vector Learning, 1999, pp. 327–352. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K. Muller, Fisher discriminant analysis with kernels, in: Proceedings of IEEE International Workshop on Neural Networks for Signal Processing, volume IX, 1999, pp. 41–48. Li, 2008, Kernel class-wise locality preserving projection, Information Sciences, 178, 1825, 10.1016/j.ins.2007.12.001 Z. Wang, X. Sun, Face recognition using kernel-based NPE, in: Proceedings of IEEE International Conference on Computer Science and Software Engineering (CSSE), 2008, pp. 802–805. Tan, 2006, Face recognition from a single image per person: a survey, Pattern Recognition, 39, 1725, 10.1016/j.patcog.2006.03.013 D. Cai, X. He, J. Han, Semi-supervised discriminant analysis, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2007, pp. 1–7. W. Zhao, R. Chellappa, P.J. Phillips, Subspace linear discriminant analysis for face recognition, Technical Report CAR-TR-914, Center for Automation Research, University of Maryland, 1999. D. Beymer, T. Poggio, Face recognition from one example view, in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 1995, pp. 500–507. Niyogi, 1998, Incorporating prior information in machine learning by creating virtual examples, Proceedings of the IEEE, 86, 2196, 10.1109/5.726787 Chen, 2004, Making FLDA applicable to face recognition with one sample per person, Pattern Recognition, 37, 1553, 10.1016/j.patcog.2003.12.010 Martinez, 2002, Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 748, 10.1109/TPAMI.2002.1008382 Tan, 2005, Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble, IEEE Transactions on Neural Networks, 16, 875, 10.1109/TNN.2005.849817 J.H. Chen, J.P. Ye, Q. Li, Integrating global and local structures: a least squares framework for dimensionality reduction, in: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1–8. Song, 2008, A unified framework for semi-supervised dimensionality reduction, Pattern Recognition, 41, 2789, 10.1016/j.patcog.2008.01.001 Li, 2006, Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Neural Networks, 17, 157, 10.1109/TNN.2005.860852 Yan, 2007, Graph embedding and extensions: a general framework for dimensionality reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 40, 10.1109/TPAMI.2007.250598 Belkin, 2006, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research, 7, 2399 Raudys, 1991, Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 252, 10.1109/34.75512 Friedman, 1989, Regularized discriminant analysis, Journal of the American Statistical Association, 84, 165, 10.1080/01621459.1989.10478752 Ji, 2008, Generalized linear discriminant analysis: a unified framework and efficient model selection, IEEE Transactions on Neural Networks, 19, 1768, 10.1109/TNN.2008.2002078 Wright, 2009, Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 210, 10.1109/TPAMI.2008.79 Basri, 2003, Lambertian reflection and linear subspaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 218, 10.1109/TPAMI.2003.1177153 Hastie, 2001 Seber, 2003 Tikhonov, 1977 Lu, 2003, Face recognition using LDA based algorithms, IEEE Transactions on Neural Networks, 14, 195, 10.1109/TNN.2002.806647 Scholkopf, 2002 B. Scholkopf, R. Herbrich, A.J. Smola, A generalized representer theorem, in: Proceedings of 14th Annual Conference on Computational Learning Theory (COLT), 2001, pp. 416–426. Bressan, 2003, Nonparametric discriminant analysis and nearest neighbor classification, Pattern Recognition Letters, 24, 2743, 10.1016/S0167-8655(03)00117-X J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Neighbourhood components analysis, in: Proceedings of the Advances in Neural Information Processing Systems (NIPS), 2005, pp. 513–520. Weinberger, 2009, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, 10, 207 Sim, 2003, The CMU pose, illumination, and expression database, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1615, 10.1109/TPAMI.2003.1251154 Lee, 2005, Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 684, 10.1109/TPAMI.2005.92 Martinez, 2001, PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 228, 10.1109/34.908974 Toh, 2008, Between classification-error approximation and weighted least-squares learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 658, 10.1109/TPAMI.2007.70730 L. Zhang, M. Yang, X. Feng, Sparse representation or collaborative representation: which helps face recognition? in: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011, pp. 471–478. Butman, 2008, Face recognition using classification-based linear projections, EURASIP Journal on Advances in Signal Processing, 1 Qiao, 2010, An empirical study of two typical locality preserving linear discriminant analysis methods, Neurocomputing, 73, 1587, 10.1016/j.neucom.2009.10.022