Robust heterogeneous discriminative analysis for face recognition with single sample per person
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Best-Rowden, 2014, Unconstrained face recognition: identifying a person of interest from a media collection, IEEE Trans. Inf. Forensics Secur., 9, 2144, 10.1109/TIFS.2014.2359577
Wolf, 2011, Face recognition in unconstrained videos with matched background similarity, 529
Bashbaghi, 2017, Dynamic ensembles of exemplar-svms for still-to-video face recognition, Pattern Recognit., 69, 61, 10.1016/j.patcog.2017.04.014
Wagner, 2012, Toward a practical face recognition system: robust alignment and illumination by sparse representation, IEEE Trans. Pattern Anal. Mach. Intell., 34, 372, 10.1109/TPAMI.2011.112
Erdogmus, 2014, Spoofing face recognition with 3D masks, IEEE Trans. Inf. Forensics Secur., 9, 1084, 10.1109/TIFS.2014.2322255
Ye, 2016, Person re-identification via ranking aggregation of similarity pulling and dissimilarity pushing, IEEE Trans. Multimed., 18, 2553, 10.1109/TMM.2016.2605058
Li, 2018, Semi-supervised region metric learning for person re-identification, Int. J. Comput. Vis., 1
Lan, 2018, Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker, IEEE Trans. Image Process., 27, 2022, 10.1109/TIP.2017.2777183
He, 2017, Robust object tracking via key patch sparse representation, IEEE Trans. Cybern., 47, 354
Zhao, 2003, Face recognition: a literature survey, ACM Comput. Surv., 35, 399, 10.1145/954339.954342
Tan, 2006, Face recognition from a single image per person: a survey, Pattern Recognit., 39, 1725, 10.1016/j.patcog.2006.03.013
Belhumeur, 1997, Eigenfaces vs. fisherfaces: recognition using class specific linear projection, IEEE Trans. Pattern Anal. Mach. Intell., 19, 711, 10.1109/34.598228
Gui, 2012, Discriminant sparse neighborhood preserving embedding for face recognition, Pattern Recognit., 45, 2884, 10.1016/j.patcog.2012.02.005
Zhou, 2017, Manifold partition discriminant analysis, IEEE Trans. Cybern., 47, 830, 10.1109/TCYB.2016.2529299
Pang, 2017, Discriminant manifold learning via sparse coding for robust feature extraction, IEEE Access, 5, 13978, 10.1109/ACCESS.2017.2730281
Wright, 2009, Robust face recognition via sparse representation, IEEE Trans. Pattern Anal. Mach. Intell., 31, 210, 10.1109/TPAMI.2008.79
Zhang, 2011, Sparse representation or collaborative representation: which helps face recognition?, 471
Gao, 2015, Neither global nor local: regularized patch-based representation for single sample per person face recognition, Int. J. Comput. Vis., 111, 365, 10.1007/s11263-014-0750-4
Deng, 2012, Extended SRC: undersampled face recognition via intraclass variant dictionary, IEEE Trans. Pattern Anal. Mach. Intell., 34, 1864, 10.1109/TPAMI.2012.30
Yang, 2013, Sparse variation dictionary learning for face recognition with a single training sample per person, 689
Yu, 2017, Discriminative multi-scale sparse coding for single-sample face recognition with occlusion, Pattern Recognit., 66, 302, 10.1016/j.patcog.2017.01.021
Gao, 2017, Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples, IEEE Trans. Image Process., 26, 2545, 10.1109/TIP.2017.2675341
Lu, 2013, Discriminative multimanifold analysis for face recognition from a single training sample per person, IEEE Trans. Pattern Anal. Mach. Intell., 35, 39, 10.1109/TPAMI.2012.70
Zhang, 2005, A new face recognition method based on SVD perturbation for single example image per person, Appl. Math. Comput., 163, 895, 10.1016/j.amc.2004.04.016
Gao, 2008, Face recognition using FLDA with single training image per person, Appl. Math. Comput., 205, 726, 10.1016/j.amc.2008.05.019
Wang, 2006, On solving the face recognition problem with one training sample per subject, Pattern Recognit., 39, 1746, 10.1016/j.patcog.2006.03.010
Deng, 2013, In defense of sparsity based face recognition, 399
Ji, 2017, Collaborative probabilistic labels for face recognition from single sample per person, Pattern Recognit., 62, 125, 10.1016/j.patcog.2016.08.007
Zhu, 2012, Multi-scale patch based collaborative representation for face recognition with margin distribution optimization, 822
Liu, 2015, Local structure-based sparse representation for face recognition, ACM Trans. Intell. Syst. Technol., 7, 2, 10.1145/2733383
Zhang, 2016, Sparse discriminative multi-manifold embedding for one-sample face identification, Pattern Recognit., 52, 249, 10.1016/j.patcog.2015.09.024
Pei, 2017, Decision pyramid classifier for face recognition under complex variations using single sample per person, Pattern Recognit., 64, 305, 10.1016/j.patcog.2016.11.016
Gottumukkal, 2004, An improved face recognition technique based on modular PCA approach, Pattern Recognit. Lett., 25, 429, 10.1016/j.patrec.2003.11.005
Chen, 2004, Making FLDA applicable to face recognition with one sample per person, Pattern Recognit., 37, 1553, 10.1016/j.patcog.2003.12.010
Yan, 2014, Multi-feature multi-manifold learning for single-sample face recognition, Neurocomputing, 143, 134, 10.1016/j.neucom.2014.06.012
Zhu, 2014, Local generic representation for face recognition with single sample per person, 34
Khadhraoui, 2018, Local generic representation for patch uLBP-based face recognition with single training sample per subject, Multimed. Tools Appl., 1
Belkin, 2001, Laplacian eigenmaps and spectral techniques for embedding and clustering, vol. 14, 585
Yan, 2009, Semi-supervised learning by sparse representation, 792
Sun, 2014, Deep learning face representation from predicting 10,000 classes, 1891
Parkhi, 2015, Deep face recognition., vol. 1, 6
Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371
Li, 2018, Distance metric optimization driven convolutional neural network for age invariant face recognition, Pattern Recognit., 75, 51, 10.1016/j.patcog.2017.10.015
B. Amos, B. Ludwiczuk, M. Satyanarayanan, et al., Openface: A General-Purpose Face Recognition Library with Mobile Applications, CMU School of Computer Science(2016).
Gao, 2015, Single sample face recognition via learning deep supervised autoencoders, IEEE Trans. Inf. Forensics Secur., 10, 2108, 10.1109/TIFS.2015.2446438
Parchami, 2017, CNNs with cross-correlation matching for face recognition in video surveillance using a single training sample per person, 1
Yang, 2017, Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person, Pattern Recognit., 66, 117, 10.1016/j.patcog.2016.12.028
Pang, 2017, Robust heterogeneous discriminative analysis for single sample per person face recognition, 2251
Cai, 2007, Spectral regression: a unified subspace learning framework for content-based image retrieval, 403
Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579
Turk, 1991, Face recognition using eigenfaces, 586
Wu, 2002, Face recognition with one training image per person, Pattern Recognit Lett, 23, 1711, 10.1016/S0167-8655(02)00134-4
Yang, 2004, Two-dimensional PCA: a new approach to appearance-based face representation and recognition, IEEE Trans. Pattern Anal. Mach. Intell., 26, 131, 10.1109/TPAMI.2004.1261097
He, 2005, Face recognition using laplacianfaces, IEEE Trans. Pattern Anal. Mach. Intell., 27, 328, 10.1109/TPAMI.2005.55
Martinez, 1998, The AR Face Database
Phillips, 2000, The FERET evaluation methodology for face-recognition algorithms, IEEE Trans. Pattern Anal. Mach. Intell., 22, 1090, 10.1109/34.879790
Gao, 2008, The CAS-PEAL large-scale chinese face database and baseline evaluations, IEEE Trans. Syst. Man Cybern., 38, 149, 10.1109/TSMCA.2007.909557
Georghiades, 2001, From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 23, 643, 10.1109/34.927464
Donoho, 2008, Fast solution of l1-norm minimization problems when the solution may be sparse, IEEE Trans. Inf. Theory, 54, 4789, 10.1109/TIT.2008.929958
Yang, 2013, Fast l1-minimization algorithms for robust face recognition, IEEE Trans. Image Process., 22, 3234, 10.1109/TIP.2013.2262292
Huang, 2007, Labeled faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
Vedaldi, 2015, Matconvnet: convolutional neural networks for matlab, 689
Wei, 2015, Undersampled face recognition via robust auxiliary dictionary learning, IEEE Trans. Image Process., 24, 1722, 10.1109/TIP.2015.2409738
Wang, 2012, Extract minimum positive and maximum negative features for imbalanced binary classification, Pattern Recognit., 45, 1136, 10.1016/j.patcog.2011.09.004