|
[1] T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. TPAMI, 28(12):2037–2041, 2006. [2] S. Xie, S. Shan, X. Chen, and J. Chen. Fusing local patterns of gabor magnitude and phase for face recognition. TIP, 19(5):1349–1361, 2010. [3] A. Asthana, T. K. Marks, M. J. Jones, K. H. Tieu, and M. V. Rohith. Fully automatic pose-invariant face recognition via 3d pose normalization. In ICCV, pages 937–944, 2011. [4] Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1701-1708). [5] Sun, Y., Wang, X., & Tang, X. (2015). Deeply learned face representations are sparse, selective, and robust. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2892-2900). [6] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October, 2007. [7] L. Wolf, T. Hassner and I. Maoz, "Face recognition in unconstrained videos with matched background similarity," CVPR 2011, Providence, RI, 2011, pp. 529-534. [8] B. F. Klare et al., "Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A," CVPR, Boston, MA, 2015, pp. 1931-1939. [9] I. Masi, S. Rawls, G. Medioni and P. Natarajan, "Pose-Aware Face Recognition in the Wild," CVPR, Las Vegas, NV, 2016, pp. 4838-4846. [10] Chen, J. C., Patel, V. M., & Chellappa, R. (2016, March). Unconstrained face verification using deep cnn features. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on (pp. 1-9). IEEE. [11] Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016, October). A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision (pp. 499-515). Springer International Publishing. [12] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, “Multi-pie,” Proc. in AFGR, pp. 1,8,17–19, Sept. 2008. [13] Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014). Learning face representation from scratch. arXiv preprint arXiv:1411.7923. [14] Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep Face Recognition." BMVC. Vol. 1. No. 3. 2015. [15] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778). [16] Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3730-3738). [17] Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., & Zhao, D. (2008). The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(1), 149-161. [18] Ricanek, K., & Tesafaye, T. (2006, April). Morph: A longitudinal image database of normal adult age-progression. In Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on (pp. 341-345). IEEE. [19] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). [20] Sun, Y., Wang, X., & Tang, X. (2014). Deep learning face representation from predicting 10,000 classes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1891-1898). [21] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. [22] Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems (pp. 1988-1996). [23] Ding, C., & Tao, D. (2015). Robust face recognition via multimodal deep face representation. Multimedia, IEEE Transactions on, 17(11), 2049-2058. [24] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In Proc. CVPR, 2015. [25] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deep-Face: Closing the gap to human-level performance in face verification. In Proc. CVPR, 2014. [26] Zhang, N., Paluri, M., Ranzato, M., Darrell, T., Bourdev, L.: Panda: Pose aligned networks for deep attribute modeling. In: CVPR. (2014) [27] Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009, September). Attribute and simile classifiers for face verification. In Computer Vision, 2009 IEEE 12th International Conference on (pp. 365-372). IEEE. [28] Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia (pp. 675-678). ACM. [29] Hsu, G. S., Chang, K. H., & Huang, S. C. (2015). Regressive Tree Structured Model for Facial Landmark Localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3855-3861). [30] Bourdev, L., Maji, S., & Malik, J. (2011, November). Describing people: A poselet-based approach to attribute classification. In Computer Vision (ICCV), 2011 IEEE International Conference on (pp. 1543-1550). IEEE. [31] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1627–1645, 2010. [32] Xiong, X., & Torre, F. (2013). Supervised descent method and its applications to face alignment. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 532-539). [33] Zhang, T. (2011). Adaptive forward-backward greedy algorithm for learning sparse representations. Information Theory, IEEE Transactions on, 57(7), 4689-4708. [34] X. Zhu, D. Ramanan. "Face detection, pose estimation and landmark localization in the wild",Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island, June 2012. [35] H. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. Pattern Analysis and Machine Intelligence, IEEE Trans., 20(1):23–38, 1998. [36] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan.”Object detection with discriminatively trained partbased models.” IEEE TPAMI, 2009. [37] A. Li, S. Shan, W. Gao, “Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition,” in TIP , vol.21, no.1, pp.305-315, Jan. 2012 [38] T. Hassner, S. Harel, E. Paz and R. Enbar, "Effective face frontalization in unconstrained images," CVPR, Boston, MA, 2015, pp. 4295-4304. [39] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255, 2009. [40] A. Asthana, S. Zafeiriou, S. Cheng and M. Pantic, "Incremental Face Alignment in the Wild," CVPR, OH, 2014, pp. 1859-1866. [41] D. Chen, X. D. Cao, L. W. Wang, F. Wen, and J. Sun. Bayesian face revisited: A joint formulation. In European Conference on Computer Vision, pages 566–579. 2012. [42] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). [43] Masi, I., Trần, A. T., Hassner, T., Leksut, J. T., & Medioni, G. (2016, October). Do we really need to collect millions of faces for effective face recognition?. In European Conference on Computer Vision (pp. 579-596). Springer International Publishing. [44] Lewis, J.P., Anjyo, K., Rhee, T., Zhang, M., Pighin, F., Deng, Z.: Practice and Theory of Blendshape Facial Models. In: Eurographics 2014 (2014) [45] H.-W. Ng, S. Winkler. A data-driven approach to cleaning large face datasets. Proc. IEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 27-30, 2014. [46] Z. Lei, D. Yi and S. Z. Li, "Learning Stacked Image Descriptor for Face Recognition," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 9, pp. 1685-1696, Sept. 2016. [47] Xi Yin and Xiaoming Liu. Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition. arXiv:1702.04710, 2017. [48] Wang, J., Cheng, Y., & Schmidt Feris, R. (2016). Walk and learn: Facial attribute representation learning from egocentric video and contextual data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2295-2304). [49] K. He, J. Sun, Convolutional neural networks at constrained time cost, in: CVPR, 2015, pp. 5353-5360. [50] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. [51] Chen, B. C., Chen, C. S., & Hsu, W. H. (2015). Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Transactions on Multimedia, 17(6), 804-815. [52] W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang. Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. TEEE TCCV, 2005. I, 6, 7
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