Face recognition via Deep Stacked Denoising Sparse Autoencoders (DSDSA)
Face recognition is still a hot topic under investigation due to many challenges of variation including the difference in poses, illumination, expression, occlusion and scenes. Recently, deep learning methods achieved remarkable results in image representation and recognition fields. Such methods ex...
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| Veröffentlicht in: | Applied mathematics and computation Jg. 355; S. 325 - 342 |
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| Sprache: | Englisch |
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15.08.2019
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| ISSN: | 0096-3003, 1873-5649 |
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| Abstract | Face recognition is still a hot topic under investigation due to many challenges of variation including the difference in poses, illumination, expression, occlusion and scenes. Recently, deep learning methods achieved remarkable results in image representation and recognition fields. Such methods extract salient features automatically from images to reduce the dimension and obtain more useful representation of raw data. In this paper, the proposed face recognition system namely Deep Stacked Denoising Sparse Autoencoders (DSDSA) combines the deep neural network technology, sparse autoencoders and denoising task. Autoencoder is used to construct a neural network that learns an approximation of an identity function by placing constraints to learn fine representations of the inputs. Autoencoders have unique capabilities in dealing with interpretation of the input data; in this way produce more meaningful results. They are successfully applied to many object recognition fields. For the classification task, two classifiers were used, namely multi-class SVM and Softmax classifier. Experimental results on known face databases including ORL, Yale, Caltech and a subset of PubFig show that the proposed system yields promising performance and achieves comparable accuracy. |
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| AbstractList | Face recognition is still a hot topic under investigation due to many challenges of variation including the difference in poses, illumination, expression, occlusion and scenes. Recently, deep learning methods achieved remarkable results in image representation and recognition fields. Such methods extract salient features automatically from images to reduce the dimension and obtain more useful representation of raw data. In this paper, the proposed face recognition system namely Deep Stacked Denoising Sparse Autoencoders (DSDSA) combines the deep neural network technology, sparse autoencoders and denoising task. Autoencoder is used to construct a neural network that learns an approximation of an identity function by placing constraints to learn fine representations of the inputs. Autoencoders have unique capabilities in dealing with interpretation of the input data; in this way produce more meaningful results. They are successfully applied to many object recognition fields. For the classification task, two classifiers were used, namely multi-class SVM and Softmax classifier. Experimental results on known face databases including ORL, Yale, Caltech and a subset of PubFig show that the proposed system yields promising performance and achieves comparable accuracy. |
| Author | Simsek, Ahmet Görgel, Pelin |
| Author_xml | – sequence: 1 givenname: Pelin surname: Görgel fullname: Görgel, Pelin email: paras@istanbul.edu.tr – sequence: 2 givenname: Ahmet surname: Simsek fullname: Simsek, Ahmet |
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| Cites_doi | 10.1109/TIFS.2015.2446438 10.1016/j.procs.2018.05.164 10.1007/s12559-016-9404-x 10.3758/BRM.42.1.351 10.1109/TCSVT.2011.2133210 10.1023/B:VISI.0000013087.49260.fb 10.1109/TPAMI.2008.79 10.1016/j.neucom.2011.08.018 10.1162/neco.2006.18.7.1527 10.1016/j.patrec.2018.04.010 10.1007/s00138-004-0144-7 10.1109/TPAMI.2005.55 10.1016/j.ijleo.2015.10.179 10.1016/j.neucom.2017.08.043 10.1162/089976698300017467 10.1109/TPAMI.2015.2448075 10.1126/science.1127647 10.1126/science.290.5500.2323 10.1016/j.neucom.2016.12.038 10.1561/2200000006 10.1016/j.procs.2017.03.153 10.1109/TMM.2015.2477042 10.1016/j.jvcir.2012.05.003 10.1016/j.patrec.2011.01.004 10.1109/34.817413 10.1109/34.598228 10.1109/TPAMI.2006.244 10.1016/j.ijleo.2017.12.072 |
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| References | C. Huang, S. Zhu, K. Yu, 2012, Large scale strongly supervised ensemble metric learning with applications to face verification and retrieval. arXiv Y. Sun, X. Wang, X. Tang, 2015, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2892–2900. Vinay, Gupta, Bharadwaj, Srinivasan, Murthy, Natarajan (bib0041) 2018; 132 Ren, Hui, Hu, Zhan (bib0013) 2018; 167 Koestinger, Wohlhart, Roth, Bischof (bib0065) 2011 Sun, Wang, Tang (bib0028) 2014 Z. Zhu, P. Luo, X. Wang, X. Tang, 2014, Recover canonical-view faces in the wild with deep neural networks. arXiv Zeng, Zhang, Song, Liu, Li, Dobaie (bib0049) 2018; 273 Zeng, Wang, Zhang, Liu, Alsaadi (bib0029) 2016; 8 Lu, Min, Gui, Zhu, Lei (bib0022) 2013; 24 Schölkopf, Smola, Müller (bib0006) 1998; 10 Chen, Huang, Liu, Zhana (bib0039) 2016; 127 He, Yan, Hu, Niyogi, Zhang (bib0057) 2005; 27 Ding, Tao (bib0047) 2015; 17 Huang, Ramesh, Berg, Learned-miller (bib0061) 2008 Vapnik (bib0012) 1998 Yin, Liu, Jin, Yang (bib0024) 2012; 77 Huang, Lee, Learned-Miller (bib0025) 2012 Beveridge, Bolme, Draper, Teixeira (bib0064) 2005; 16 Ortiz, Wright, Shah (bib0060) June 2013 Jain, Li (bib0001) 2011 Mika, Ratsch, Weston, Scholkopf, Mullers (bib0007) 1999 Ebner, Riediger, Lindenberger (bib0063) 2010; 42 Biswas, Sil (bib0011) 2017 Ahonen, Hadid, Pietikainen (bib0008) 2006; 28 Ho, Yang, Lim, Lee, Kriegman (bib0014) 2003 Yang, Zhang (bib0021) 2010 Biswas, Sil, Maity (bib0038) 2017 . Hinton, Salakhutdinov (bib0043) 2006; 313 Hinton, Osindero, Teh (bib0055) 2006; 18 Kumar, Berg, Belhumeur, Nayar (bib0056) 2009 Luo, Shen, Hu, Deng, Hu, Guan (bib0042) 2017; 107 Déniz, Bueno, Salido, De la Torre (bib0010) 2011; 32 Wright, Yang, Ganesh, Sastry, Ma (bib0018) 2009; 31 Chopra, Hadsell, LeCun (bib0033) 2005; 1 Cinbis, Verbeek, Schmid (bib0017) 2011 Baltrusaitis, Robinson, Morency (bib0054) 2013 Taigman, Yang, Ranzato, Wolf (bib0026) 2014 Zhang, Yang, Feng (bib0019) 2011 Liao, Jain, Li (bib0002) 2016; 38 Boukabou, Ghouti, Bouridane (bib0009) 2006 Liu, Wang, Liu, Zeng, Liu, Alsaadi (bib0031) 2017; 234 Gravelines (bib0051) 2014 Bengio (bib0050) 2009; 2 Turk, Pentland (bib0004) 1991 Schroff, Kalenichenko, Philbin (bib0035) 2015 Jain, Shishir, Kumar, Shamsolmoali, Zareapoor (bib0040) 2018 Belhumeur, Hespanha, Kriegman (bib0003) 1997; 19 Sudha, Mohan, Meher (bib0058) 2011; 21 Li, Gao, Wang (bib0048) 2017; 132 Lyons, Budynek, Akamatsu (bib0062) 1999; 21 Yu, Huang, Zhang, Yan, Metaxas (bib0053) 2013 Zhang, Shan, Kan, Chen (bib0045) 2014 T. Sim, R. Sukthankar, M. Mullin, S. Baluja, 1970, High-Performance Memory-based Face Recognition for Visitor Identification. Gao, Zhang, Jia, Lu, Zhang (bib0020) 2015; 10 Parkhi, Vedaldi, Zisserman (bib0036) 2015, September Gao, Tsang, Chia (bib0046) 2010 Zhang, Li, Zhu (bib0037) 2015 Viola, Jones (bib0052) 2004; 57 Guillaumin, Verbeek, Schmid (bib0015) 2009, September Kan, Shan, Chang, Chen (bib0044) 2014 Y. Sun, D. Liang, X. Wang, X. Tang, 2015, Deepid3: face recognition with very deep neural networks. arXiv Min, Dugelay (bib0023) 2011 Roweis, Saul (bib0005) 2000; 290 Sun, Wang, Tang (bib0027) 2013 Gao (10.1016/j.amc.2019.02.071_bib0046) 2010 Ahonen (10.1016/j.amc.2019.02.071_bib0008) 2006; 28 Zeng (10.1016/j.amc.2019.02.071_bib0049) 2018; 273 Yang (10.1016/j.amc.2019.02.071_bib0021) 2010 10.1016/j.amc.2019.02.071_bib0016 Baltrusaitis (10.1016/j.amc.2019.02.071_bib0054) 2013 Huang (10.1016/j.amc.2019.02.071_bib0061) 2008 Roweis (10.1016/j.amc.2019.02.071_bib0005) 2000; 290 Sun (10.1016/j.amc.2019.02.071_bib0027) 2013 Ho (10.1016/j.amc.2019.02.071_bib0014) 2003 Lu (10.1016/j.amc.2019.02.071_bib0022) 2013; 24 Yu (10.1016/j.amc.2019.02.071_bib0053) 2013 Ebner (10.1016/j.amc.2019.02.071_bib0063) 2010; 42 Yin (10.1016/j.amc.2019.02.071_bib0024) 2012; 77 Vinay (10.1016/j.amc.2019.02.071_bib0041) 2018; 132 Turk (10.1016/j.amc.2019.02.071_bib0004) 1991 Schroff (10.1016/j.amc.2019.02.071_bib0035) 2015 Min (10.1016/j.amc.2019.02.071_bib0023) 2011 Viola (10.1016/j.amc.2019.02.071_bib0052) 2004; 57 Hinton (10.1016/j.amc.2019.02.071_bib0055) 2006; 18 10.1016/j.amc.2019.02.071_bib0059 Ortiz (10.1016/j.amc.2019.02.071_bib0060) 2013 Luo (10.1016/j.amc.2019.02.071_bib0042) 2017; 107 Jain (10.1016/j.amc.2019.02.071_bib0040) 2018 Beveridge (10.1016/j.amc.2019.02.071_bib0064) 2005; 16 Zhang (10.1016/j.amc.2019.02.071_bib0019) 2011 Boukabou (10.1016/j.amc.2019.02.071_bib0009) 2006 Belhumeur (10.1016/j.amc.2019.02.071_bib0003) 1997; 19 Kumar (10.1016/j.amc.2019.02.071_bib0056) 2009 Kan (10.1016/j.amc.2019.02.071_bib0044) 2014 Schölkopf (10.1016/j.amc.2019.02.071_bib0006) 1998; 10 Biswas (10.1016/j.amc.2019.02.071_bib0011) 2017 Sun (10.1016/j.amc.2019.02.071_bib0028) 2014 Biswas (10.1016/j.amc.2019.02.071_bib0038) 2017 Sudha (10.1016/j.amc.2019.02.071_bib0058) 2011; 21 Guillaumin (10.1016/j.amc.2019.02.071_bib0015) 2009 Ding (10.1016/j.amc.2019.02.071_bib0047) 2015; 17 Gravelines (10.1016/j.amc.2019.02.071_bib0051) 2014 Liu (10.1016/j.amc.2019.02.071_bib0031) 2017; 234 Taigman (10.1016/j.amc.2019.02.071_bib0026) 2014 Déniz (10.1016/j.amc.2019.02.071_bib0010) 2011; 32 Bengio (10.1016/j.amc.2019.02.071_bib0050) 2009; 2 He (10.1016/j.amc.2019.02.071_bib0057) 2005; 27 Wright (10.1016/j.amc.2019.02.071_bib0018) 2009; 31 Parkhi (10.1016/j.amc.2019.02.071_bib0036) 2015 Koestinger (10.1016/j.amc.2019.02.071_bib0065) 2011 Ren (10.1016/j.amc.2019.02.071_bib0013) 2018; 167 Zeng (10.1016/j.amc.2019.02.071_bib0029) 2016; 8 10.1016/j.amc.2019.02.071_bib0034 Liao (10.1016/j.amc.2019.02.071_bib0002) 2016; 38 Mika (10.1016/j.amc.2019.02.071_bib0007) 1999 10.1016/j.amc.2019.02.071_bib0030 10.1016/j.amc.2019.02.071_bib0032 Gao (10.1016/j.amc.2019.02.071_bib0020) 2015; 10 Hinton (10.1016/j.amc.2019.02.071_bib0043) 2006; 313 Li (10.1016/j.amc.2019.02.071_bib0048) 2017; 132 Chen (10.1016/j.amc.2019.02.071_bib0039) 2016; 127 Chopra (10.1016/j.amc.2019.02.071_bib0033) 2005; 1 Lyons (10.1016/j.amc.2019.02.071_bib0062) 1999; 21 Cinbis (10.1016/j.amc.2019.02.071_bib0017) 2011 Huang (10.1016/j.amc.2019.02.071_bib0025) 2012 Zhang (10.1016/j.amc.2019.02.071_bib0045) 2014 Vapnik (10.1016/j.amc.2019.02.071_bib0012) 1998 Jain (10.1016/j.amc.2019.02.071_bib0001) 2011 Zhang (10.1016/j.amc.2019.02.071_bib0037) 2015 |
| References_xml | – volume: 27 start-page: 328 year: 2005 end-page: 340 ident: bib0057 article-title: Face recognition using laplacianfaces publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 21 start-page: 1071 year: 2011 end-page: 1084 ident: bib0058 article-title: A self-configurable systolic architecture for face recognition system based on principal component neural network publication-title: IEEE Trans. Circuits Syst. Video Technol. – year: 2017 ident: bib0011 article-title: An efficient face recognition method using contourlet and curvelet transform publication-title: J.King Saud Univ. Comput. Inf. Sci. – start-page: 471 year: 2011 end-page: 478 ident: bib0019 article-title: Sparse representation or collaborative representation: which helps face recognition? publication-title: Proceedings of the IEEE in Computer vision (ICCV) – volume: 17 start-page: 2049 year: 2015 end-page: 2058 ident: bib0047 article-title: Robust face recognition via multimodal deep face representation publication-title: IEEE Trans. Multimed. – volume: 28 start-page: 2037 year: 2006 end-page: 2041 ident: bib0008 article-title: Face description with local binary patterns application to face recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 354 year: 2013 end-page: 361 ident: bib0054 article-title: Constrained local neural fields for robust facial landmark detection in the wild publication-title: Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops (ICCVW) – year: 2008 ident: bib0061 article-title: Labeled faces in the wild: a database for studying face recognition in unconstrained environments publication-title: Proceedings of the ECCV Workshop on Faces in Real-life Images, 2008 – start-page: 2144 year: 2011 end-page: 2151 ident: bib0065 article-title: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization publication-title: Proceedings of the IEEE International Workshop on Benchmarking Facial Image Analysis Technologies – start-page: 1701 year: 2014 end-page: 1708 ident: bib0026 article-title: Deepface: closing the gap to human-level performance in face verification publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – year: 2017 ident: bib0038 article-title: On prediction error compressive sensing image reconstruction for face recognition publication-title: Comput. Electr. Eng. – year: 2018 ident: bib0040 article-title: Hybrid deep neural networks for face emotion recognition publication-title: Pattern Recognit. Lett. – volume: 77 start-page: 120 year: 2012 end-page: 128 ident: bib0024 article-title: Kernel sparse representation based classification publication-title: Neurocomputing – start-page: 1891 year: 2014 end-page: 1898 ident: bib0028 article-title: Deep learning face representation from predicting 10,000 classes publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 16 start-page: 128 year: 2005 end-page: 138 ident: bib0064 article-title: The CSU face identification evaluation system: its purpose features and structure publication-title: Mach. Vis. Appl. – reference: Y. Sun, D. Liang, X. Wang, X. Tang, 2015, Deepid3: face recognition with very deep neural networks. arXiv: – volume: 167 start-page: 7 year: 2018 end-page: 14 ident: bib0013 article-title: A weighted sparse neighbor representation based on Gaussian kernel function to face recognition publication-title: Optik – volume: 19 start-page: 711 year: 1997 end-page: 720 ident: bib0003 article-title: Eigenfaces vs. fisherfaces: recognition using class specific linear projection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 31 start-page: 210 year: 2009 end-page: 227 ident: bib0018 article-title: Robust face recognition via sparse representation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 594 year: 2015 end-page: 598 ident: bib0037 article-title: Deep neural network for face recognition based on sparse autoencoder publication-title: Proceedings of the Eighth International Congress on Image and Signal Processing (CISP) – year: 1998 ident: bib0012 article-title: Statistical Learning Theory – start-page: 448 year: 2010 end-page: 461 ident: bib0021 article-title: Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary publication-title: Proceedings of the European Conference on Computer Vision – start-page: 6 year: 2015, September ident: bib0036 article-title: Deep face recognition publication-title: Proceedings of the BMVC – volume: 21 start-page: 1357 year: 1999 end-page: 1362 ident: bib0062 article-title: Automatic classification of single facial images publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 1 year: 2010 end-page: 14 ident: bib0046 article-title: Kernel sparse representation for image classification and face recognition publication-title: Proceedings of the European Conference on Computer Vision – volume: 107 start-page: 715 year: 2017 end-page: 720 ident: bib0042 article-title: A Deep convolution neural network model for vehicle recognition and face recognition publication-title: Proc. Comput. Sci. – volume: 24 start-page: 111 year: 2013 end-page: 116 ident: bib0022 article-title: Face recognition via weighted sparse representation publication-title: J. Vis. Commun. Image Represent. – reference: Y. Sun, X. Wang, X. Tang, 2015, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2892–2900. – reference: Z. Zhu, P. Luo, X. Wang, X. Tang, 2014, Recover canonical-view faces in the wild with deep neural networks. arXiv: – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib0055 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – start-page: 815 year: 2015 end-page: 823 ident: bib0035 article-title: Facenet: a unified embedding for face recognition and clustering publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 273 start-page: 643 year: 2018 end-page: 649 ident: bib0049 article-title: Facial expression recognition via learning deep sparse autoencoders publication-title: Neurocomputing – volume: 2 start-page: 1 year: 2009 end-page: 127 ident: bib0050 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. – volume: 10 start-page: 1299 year: 1998 end-page: 1319 ident: bib0006 article-title: Nonlinear component analysis as a kernel eigenvalue problem publication-title: Neural Comput. – reference: T. Sim, R. Sukthankar, M. Mullin, S. Baluja, 1970, High-Performance Memory-based Face Recognition for Visitor Identification. – start-page: 498 year: 2009, September end-page: 505 ident: bib0015 article-title: Is that you? Metric learning approaches for face identification publication-title: Proceedings of the IEEE Twelfth International Conference on Computer Vision – year: 2014 ident: bib0051 article-title: Deep learning via stacked sparse autoencoders for automated voxel-wise brain parcellation based on functional connectivity publication-title: Proceedings of the Electronic Thesis and Dissertation Repository – start-page: 1883 year: 2014 end-page: 1890 ident: bib0044 article-title: Stacked progressive auto-encoders for face recognition across poses publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 1 year: 2003 end-page: 11 ident: bib0014 article-title: Clustering appearances of objects under varying illumination conditions publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – volume: 290 start-page: 2323 year: 2000 end-page: 2326 ident: bib0005 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science – start-page: 2518 year: 2012 end-page: 2525 ident: bib0025 article-title: Learning hierarchical representations for face verification with convolutional deep belief networks publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) – volume: 132 start-page: 76 year: 2018 end-page: 83 ident: bib0041 article-title: Deep learning on binary patterns for face recognition publication-title: Proc. Comput. Sci. – start-page: 465 year: 2006 end-page: 468 ident: bib0009 article-title: Face recognition using a Gabor filter bank approach publication-title: First NASA/ESA Conference on Adaptive Hardware and Systems – start-page: 1489 year: 2013 end-page: 1496 ident: bib0027 article-title: Hybrid deep learning for face verification publication-title: Proceedings of the IEEE International Conference on Computer Vision (ICCV) – start-page: 41 year: 1999 end-page: 48 ident: bib0007 article-title: Fisher discriminant analysis with kernels publication-title: Neural Networks for Signal Processing IX, Proceedings of the 1999 IEEE Signal Processing Society Workshop – volume: 8 start-page: 684 year: 2016 end-page: 692 ident: bib0029 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cogn. Comput. – start-page: 1 year: 2014 end-page: 16 ident: bib0045 article-title: Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment publication-title: Proceedings of the European Conference on Computer Vision – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0043 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 42 start-page: 351 year: 2010 end-page: 362 ident: bib0063 article-title: FACES—A database of facial expressions in young, middle-aged, and older women and men: development and validation publication-title: Behav. Res. Methods – volume: 127 start-page: 946 year: 2016 end-page: 954 ident: bib0039 article-title: Multi-pose face ensemble classification aided by Gabor features and deep belief nets publication-title: Optik - Int. J. Light Electron Opt. – volume: 32 start-page: 1598 year: 2011 end-page: 1603 ident: bib0010 article-title: Face recognition using histograms of oriented gradients publication-title: Pattern Recognit. Lett. – volume: 57 start-page: 137 year: 2004 end-page: 154 ident: bib0052 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. – year: 2009 ident: bib0056 article-title: Attribute and simile classifiers for face verification publication-title: Proceedings of the International Conference on Computer Vision (ICCV) – volume: 1 start-page: 539 year: 2005 end-page: 546 ident: bib0033 article-title: Learning a similarity metric discriminatively, with application to face verification publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR' 05) – volume: 10 start-page: 2108 year: 2015 end-page: 2118 ident: bib0020 article-title: Single sample face recognition via learning deep supervised autoencoders publication-title: IEEE Trans. Inf. Forensics and Secur. – start-page: 1 year: 2011 end-page: 6 ident: bib0023 article-title: Improved combination of LBP and sparse representation based classification (SRC) for face recognition publication-title: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) – volume: 132 start-page: 243 year: 2017 end-page: 246 ident: bib0048 article-title: Face recognition based on deep autoencoder networks with dropout publication-title: Adv. Intell. Syst. Res. – start-page: 3531 year: June 2013 end-page: 3538 ident: bib0060 article-title: , Face recognition in movie trailers via mean sequence sparse representation-based classification publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 1944 year: 2013 end-page: 1951 ident: bib0053 article-title: Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model publication-title: Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV) – reference: C. Huang, S. Zhu, K. Yu, 2012, Large scale strongly supervised ensemble metric learning with applications to face verification and retrieval. arXiv: – reference: . – start-page: 1559 year: 2011 end-page: 1566 ident: bib0017 article-title: Unsupervised metric learning for face identification in TV video publication-title: Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV) – volume: 38 start-page: 211 year: 2016 end-page: 223 ident: bib0002 article-title: A fast and accurate unconstrained face detector publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 234 start-page: 11 year: 2017 end-page: 26 ident: bib0031 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing – year: 2011 ident: bib0001 article-title: Handbook of Face Recognition – start-page: 586 year: 1991 end-page: 591 ident: bib0004 article-title: Face recognition using eigenfaces publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR'91 – year: 1998 ident: 10.1016/j.amc.2019.02.071_bib0012 – ident: 10.1016/j.amc.2019.02.071_bib0032 – volume: 1 start-page: 539 year: 2005 ident: 10.1016/j.amc.2019.02.071_bib0033 article-title: Learning a similarity metric discriminatively, with application to face verification – volume: 10 start-page: 2108 issue: 10 year: 2015 ident: 10.1016/j.amc.2019.02.071_bib0020 article-title: Single sample face recognition via learning deep supervised autoencoders publication-title: IEEE Trans. Inf. Forensics and Secur. doi: 10.1109/TIFS.2015.2446438 – start-page: 448 year: 2010 ident: 10.1016/j.amc.2019.02.071_bib0021 article-title: Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary – start-page: 594 year: 2015 ident: 10.1016/j.amc.2019.02.071_bib0037 article-title: Deep neural network for face recognition based on sparse autoencoder – year: 2009 ident: 10.1016/j.amc.2019.02.071_bib0056 article-title: Attribute and simile classifiers for face verification – start-page: 1559 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0017 article-title: Unsupervised metric learning for face identification in TV video – volume: 132 start-page: 76 year: 2018 ident: 10.1016/j.amc.2019.02.071_bib0041 article-title: Deep learning on binary patterns for face recognition publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2018.05.164 – start-page: 1 year: 2010 ident: 10.1016/j.amc.2019.02.071_bib0046 article-title: Kernel sparse representation for image classification and face recognition – ident: 10.1016/j.amc.2019.02.071_bib0059 – start-page: 1 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0023 article-title: Improved combination of LBP and sparse representation based classification (SRC) for face recognition – year: 2017 ident: 10.1016/j.amc.2019.02.071_bib0038 article-title: On prediction error compressive sensing image reconstruction for face recognition publication-title: Comput. Electr. Eng. – volume: 8 start-page: 684 issue: 4 year: 2016 ident: 10.1016/j.amc.2019.02.071_bib0029 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cogn. Comput. doi: 10.1007/s12559-016-9404-x – volume: 42 start-page: 351 issue: 1 year: 2010 ident: 10.1016/j.amc.2019.02.071_bib0063 article-title: FACES—A database of facial expressions in young, middle-aged, and older women and men: development and validation publication-title: Behav. Res. Methods doi: 10.3758/BRM.42.1.351 – volume: 21 start-page: 1071 issue: 8 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0058 article-title: A self-configurable systolic architecture for face recognition system based on principal component neural network publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2011.2133210 – start-page: 1489 year: 2013 ident: 10.1016/j.amc.2019.02.071_bib0027 article-title: Hybrid deep learning for face verification – volume: 57 start-page: 137 issue: 2 year: 2004 ident: 10.1016/j.amc.2019.02.071_bib0052 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000013087.49260.fb – volume: 31 start-page: 210 issue: 2 year: 2009 ident: 10.1016/j.amc.2019.02.071_bib0018 article-title: Robust face recognition via sparse representation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.79 – volume: 77 start-page: 120 issue: 1 year: 2012 ident: 10.1016/j.amc.2019.02.071_bib0024 article-title: Kernel sparse representation based classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.08.018 – start-page: 1701 year: 2014 ident: 10.1016/j.amc.2019.02.071_bib0026 article-title: Deepface: closing the gap to human-level performance in face verification – start-page: 6 year: 2015 ident: 10.1016/j.amc.2019.02.071_bib0036 article-title: Deep face recognition – year: 2008 ident: 10.1016/j.amc.2019.02.071_bib0061 article-title: Labeled faces in the wild: a database for studying face recognition in unconstrained environments – year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0001 – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: 10.1016/j.amc.2019.02.071_bib0055 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – start-page: 498 year: 2009 ident: 10.1016/j.amc.2019.02.071_bib0015 article-title: Is that you? Metric learning approaches for face identification – year: 2018 ident: 10.1016/j.amc.2019.02.071_bib0040 article-title: Hybrid deep neural networks for face emotion recognition publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.04.010 – start-page: 41 year: 1999 ident: 10.1016/j.amc.2019.02.071_bib0007 article-title: Fisher discriminant analysis with kernels – volume: 16 start-page: 128 issue: 2 year: 2005 ident: 10.1016/j.amc.2019.02.071_bib0064 article-title: The CSU face identification evaluation system: its purpose features and structure publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-004-0144-7 – volume: 27 start-page: 328 issue: 3 year: 2005 ident: 10.1016/j.amc.2019.02.071_bib0057 article-title: Face recognition using laplacianfaces publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.55 – volume: 127 start-page: 946 issue: 2 year: 2016 ident: 10.1016/j.amc.2019.02.071_bib0039 article-title: Multi-pose face ensemble classification aided by Gabor features and deep belief nets publication-title: Optik - Int. J. Light Electron Opt. doi: 10.1016/j.ijleo.2015.10.179 – volume: 273 start-page: 643 year: 2018 ident: 10.1016/j.amc.2019.02.071_bib0049 article-title: Facial expression recognition via learning deep sparse autoencoders publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.08.043 – year: 2017 ident: 10.1016/j.amc.2019.02.071_bib0011 article-title: An efficient face recognition method using contourlet and curvelet transform publication-title: J.King Saud Univ. Comput. Inf. Sci. – start-page: 1944 year: 2013 ident: 10.1016/j.amc.2019.02.071_bib0053 article-title: Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model – start-page: 465 year: 2006 ident: 10.1016/j.amc.2019.02.071_bib0009 article-title: Face recognition using a Gabor filter bank approach – ident: 10.1016/j.amc.2019.02.071_bib0034 – ident: 10.1016/j.amc.2019.02.071_bib0030 – start-page: 471 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0019 article-title: Sparse representation or collaborative representation: which helps face recognition? – volume: 10 start-page: 1299 issue: 5 year: 1998 ident: 10.1016/j.amc.2019.02.071_bib0006 article-title: Nonlinear component analysis as a kernel eigenvalue problem publication-title: Neural Comput. doi: 10.1162/089976698300017467 – volume: 38 start-page: 211 issue: 2 year: 2016 ident: 10.1016/j.amc.2019.02.071_bib0002 article-title: A fast and accurate unconstrained face detector publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2448075 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.amc.2019.02.071_bib0043 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 290 start-page: 2323 issue: 5500 year: 2000 ident: 10.1016/j.amc.2019.02.071_bib0005 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science doi: 10.1126/science.290.5500.2323 – volume: 234 start-page: 11 year: 2017 ident: 10.1016/j.amc.2019.02.071_bib0031 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.038 – start-page: 2144 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0065 article-title: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization – start-page: 1883 year: 2014 ident: 10.1016/j.amc.2019.02.071_bib0044 article-title: Stacked progressive auto-encoders for face recognition across poses – volume: 2 start-page: 1 issue: 1 year: 2009 ident: 10.1016/j.amc.2019.02.071_bib0050 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. doi: 10.1561/2200000006 – start-page: 1891 year: 2014 ident: 10.1016/j.amc.2019.02.071_bib0028 article-title: Deep learning face representation from predicting 10,000 classes – start-page: 354 year: 2013 ident: 10.1016/j.amc.2019.02.071_bib0054 article-title: Constrained local neural fields for robust facial landmark detection in the wild – volume: 107 start-page: 715 year: 2017 ident: 10.1016/j.amc.2019.02.071_bib0042 article-title: A Deep convolution neural network model for vehicle recognition and face recognition publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2017.03.153 – start-page: 815 year: 2015 ident: 10.1016/j.amc.2019.02.071_bib0035 article-title: Facenet: a unified embedding for face recognition and clustering – start-page: 3531 year: 2013 ident: 10.1016/j.amc.2019.02.071_bib0060 article-title: , Face recognition in movie trailers via mean sequence sparse representation-based classification – volume: 17 start-page: 2049 issue: 11 year: 2015 ident: 10.1016/j.amc.2019.02.071_bib0047 article-title: Robust face recognition via multimodal deep face representation publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2015.2477042 – start-page: 2518 year: 2012 ident: 10.1016/j.amc.2019.02.071_bib0025 article-title: Learning hierarchical representations for face verification with convolutional deep belief networks – start-page: 1 year: 2003 ident: 10.1016/j.amc.2019.02.071_bib0014 article-title: Clustering appearances of objects under varying illumination conditions – year: 2014 ident: 10.1016/j.amc.2019.02.071_bib0051 article-title: Deep learning via stacked sparse autoencoders for automated voxel-wise brain parcellation based on functional connectivity – volume: 24 start-page: 111 issue: 2 year: 2013 ident: 10.1016/j.amc.2019.02.071_bib0022 article-title: Face recognition via weighted sparse representation publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2012.05.003 – start-page: 1 year: 2014 ident: 10.1016/j.amc.2019.02.071_bib0045 article-title: Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment – ident: 10.1016/j.amc.2019.02.071_bib0016 – volume: 32 start-page: 1598 year: 2011 ident: 10.1016/j.amc.2019.02.071_bib0010 article-title: Face recognition using histograms of oriented gradients publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2011.01.004 – volume: 21 start-page: 1357 issue: 12 year: 1999 ident: 10.1016/j.amc.2019.02.071_bib0062 article-title: Automatic classification of single facial images publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.817413 – volume: 19 start-page: 711 issue: 7 year: 1997 ident: 10.1016/j.amc.2019.02.071_bib0003 article-title: Eigenfaces vs. fisherfaces: recognition using class specific linear projection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.598228 – volume: 132 start-page: 243 year: 2017 ident: 10.1016/j.amc.2019.02.071_bib0048 article-title: Face recognition based on deep autoencoder networks with dropout publication-title: Adv. Intell. Syst. Res. – volume: 28 start-page: 2037 issue: 12 year: 2006 ident: 10.1016/j.amc.2019.02.071_bib0008 article-title: Face description with local binary patterns application to face recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2006.244 – volume: 167 start-page: 7 year: 2018 ident: 10.1016/j.amc.2019.02.071_bib0013 article-title: A weighted sparse neighbor representation based on Gaussian kernel function to face recognition publication-title: Optik doi: 10.1016/j.ijleo.2017.12.072 – start-page: 586 year: 1991 ident: 10.1016/j.amc.2019.02.071_bib0004 article-title: Face recognition using eigenfaces |
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