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Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion.
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Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in Proc. Eur. Conf. Comput. Vis. , 2012, pp. 766–779
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Y. Hu, A. S. Mian, and R. Owens, “Face recognition using sparse approximated nearest points between image sets,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 10, pp. 1992–2004, 2012
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J. R. Beveridge, J. Phillips, D. S. Bolme, B. Draper, G. H. Givens, Y. M. Lui, M. N. Teli, H. Zhang, W. T. Scruggs, K. W. Bowyer et al. , “The challenge of face recognition from digital point-and-shoot cameras,” in Proc. IEEE Int. Conf. Biometrics, Theory, Appl. Syst. , 2013, pp. 1–8
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J. Phillips, J. R. Beveridge, D. S. Bolme, B. Draper, G. H. Givens, Y. M. Lui, S. Cheng, M. N. Teli, H. Zhang et al. , “On the existence of face quality measures,” in Proc. IEEE Int. Conf. Biometrics, Theory, Appl. Syst. , 2013, pp. 1–8
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C. H. Chan, M. A. Tahir, J. Kittler, and M. Pietikainen, “Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 5, pp. 1164–1177, 2013
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Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2013, pp. 3476–3483
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P. Zhu, W. Zuo, L. Zhang, S. C.-K. Shiu, and D. Zhang, “Image set-based collaborative representation for face recognition,” IEEE Trans. Inf. Forensics Security , vol. 9, no. 7, pp. 1120–1132, 2014
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O. M. Parkhi, K. Simonyan, A. Vedaldi, and A. Zisserman, “A compact and discriminative face track descriptor,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 1693–1700
2014
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H. Li, G. Hua, X. Shen, Z. Lin, and J. Brandt, “Eigen-pep for video face recognition,” in Proc. Asian. Conf. Comput. Vis. , 2014, pp. 17–33
2014
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J. Lu, G. Wang, W. Deng, P. Moulin, and J. Zhou, “Multi-manifold deep metric learning for image set classification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 1137–1145
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R. Girshick, F. Iandola, T. Darrell, and J. Malik, “Deformable part models are convolutional neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 437–446
2015
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L. Wan, D. Eigen, and R. Fergus, “End-to-end integration of a convolution network, deformable parts model and non-maximum suppression,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 851–859
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Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 1701–1708
2014
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J. Hu, J. Lu, and Y.-P. Tan, “Discriminative deep metric learning for face verification in the wild,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 1875–1882
2014
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P.-A. Savalle, S. Tsogkas, G. Papandreou, and I. Kokkinos, “Deformable part models with cnn features,” in Proc. Europ. Conf. Comput. Vis., Parts and Attributes Workshop , 2014, pp. 1–5
2014
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N. Zhang, J. Donahue, R. Girshick, and T. Darrell, “Part-based r-cnns for fine-grained category detection,” in Proc. Europ. Conf. Comput. Vis. , 2014, pp. 834–849
2014
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R. Wang and D. Tao, “Recent progress in image deblurring,” arXiv preprint arXiv:1409.6838 , 2014
2014
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L. Xu, J. S. Ren, C. Liu, and J. Jia, “Deep convolutional neural network for image deconvolution,” in Proc. Adv. Neural Inform. Process. Syst. , 2014, pp. 1790–1798
2014
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Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proc. ACM Int. Conf. Multimedia , 2014, pp. 675–678
2014
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2014
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T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in Proc. IEEE Int. Conf. Comput. Vis. , 2015, pp. 1449–1457
2015
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J. Sun, W. Cao, Z. Xu, and J. Ponce, “Learning a convolutional neural network for non-uniform motion blur removal,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 769–777
2015
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 1–9
2015
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H. Elad and N. Ailon, “Deep metric learning using triplet network,” in Proc. Int. Workshop Similarity-Based Pattern Recognit. , 2015, pp. 84–92
2015
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C. Ding and D. Tao, “Robust face recognition via multimodal deep face representation,” IEEE Trans. Multimedia , vol. 17, no. 11, pp. 2049–2058, 2015
2015
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E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer, “Discriminative learning of deep convolutional feature point descriptors,” in Proc. IEEE Int. Conf. Comput. Vis. , 2015, pp. 118–126
2015
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2015
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Z. Huang, R. Wang, S. Shan, and X. Chen, “Face recognition on large-scale video in the wild with hybrid euclidean-and-riemannian metric learning,” Pattern Recognit. , vol. 48, no. 10, pp. 3113–3124, 2015
2015
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2015
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C. Ding, J. Choi, D. Tao, and L. S. Davis, “Multi-directional multi-level dual-cross patterns for robust face recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 3, pp. 518–531, 2016
2016
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M. Shao, D. Tang, Y. Liu, and T.-K. Kim, “A comparative study of video-based object recognition from an egocentric viewpoint,” Neurocomputing , vol. 171, pp. 982–990, 2016
2016
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C. Ding and D. Tao, “A comprehensive survey on pose-invariant face recognition,” ACM Trans. Intell. Syst. Technol. , vol. 7, pp. 37:1–37:42, 2016
2016
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W. Yang, W. Ouyang, H. Li, and X. Wang, “End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 3073–3082
2016
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S. Huang, Z. Xu, D. Tao, and Y. Zhang, “Part-stacked cnn for fine-grained visual categorization,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 1173–1182
2016
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W. Scheirer, P. Flynn, C. Ding, G. Guo et al. , “Report on the btas 2016 video person recognition evaluation,” in Proc. IEEE Int. Conf. Biometrics, Theory, Appl. Syst. , 2016, pp. 1–8
2016
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D. Chen, X. Cao, D. Wipf, F. Wen, and J. Sun, “An efficient joint formulation for bayesian face verification,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 1, pp. 32–46, 2017
2017
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J. Max, K. Simonyan, and A. Zisserman, “Spatial transformer networks,” in Adv. Neural Inform. Process. Syst. , 2015, pp. 2017–2025
2025
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W. Ouyang and X. Wang, “Joint deep learning for pedestrian detection,” in Proc. IEEE Int. Conf. Comput. Vis. , 2013, pp. 2056–2063
2063
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