Neural aggregation network for video face recognition

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 5216-5225

  Yang, Jiaolong ^a,b,c   Ren, Peiran ^a   Zhang, Dongqing ^a   Chen, Dong ^a   Wen, Fang ^a   Li, Hongdong ^b   Hua, Gang ^a  

^a MICROSOFT RESEARCH   (United States)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DEEP NEURAL NETWORKS; NEURAL NETWORKS;

AGGREGATION METHODS; AGGREGATION NETWORK; ATTENTION MECHANISMS; DEEP CONVOLUTIONAL NEURAL NETWORKS; FACE RECOGNITION BENCHMARKS; FEATURE EMBEDDING; FEATURE REPRESENTATION; STATE OF THE ART;

FACE RECOGNITION;

EID: 85044266593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.554     Document Type: Conference Paper

References (43)

1. W. AbdAlmageed, Y. Wu, S. Rawls, S. Harel, T. Hassner, I. Masi, J. Choi, J. Lekust, J. Kim, P. Natarajan, et al. Face recognition using deep multi-pose representations. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. 5, 6
2. O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla, and T. Darrell. Face recognition with image sets using manifold density divergence. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 581-588, 2005. 2
3. H. Cevikalp and B. Triggs. Face recognition based on image sets. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2567-2573, 2010. 4
4. D. Chen, G. Hua, F. Wen, and J. Sun. Supervised transformer network for efficient face detection. In European Conference on Computer Vision (ECCV), pages 122-138, 2016. 6
5. D. Chen, S. Ren, Y. Wei, X. Cao, and J. Sun. Joint cascade face detection and alignment. In European Conference on Computer Vision (ECCV), pages 109-122. 2014. 5
6. J.-C. Chen, V. M. Patel, and R. Chellappa. Unconstrained face verification using deep cnn features. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. 6
7. J.-C. Chen, R. Ranjan, A. Kumar, C.-H. Chen, V. Patel, and R. Chellappa. An end-to-end system for unconstrained face verification with deep convolutional neural networks. In IEEE International Conference on Computer Vision Workshops, pages 118-126, 2015. 2, 6
8. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 539-546, 2005. 5
9. A. R. Chowdhury, T.-Y. Lin, S. Maji, and E. Learned-Miller. One-to-many face recognition with bilinear cnns. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. 2, 6
10. N. Crosswhite, J. Byrne, O. M. Parkhi, C. Stauffer, Q. Cao, and A. Zisserman. Template adaptation for face verification and identification. arXiv preprint arXiv:1603.03958, 2016. 1, 5, 6, 7
11. Z. Cui, W. Li, D. Xu, S. Shan, and X. Chen. Fusing robust face region descriptors via multiple metric learning for face recognition in the wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3554-3561, 2013. 1
12. A. Graves, G. Wayne, and I. Danihelka. Neural turing machines. CoRR, abs/1410.5401, 2014. 2, 3
13. R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 1735-1742, 2006. 5
14. J. Hu, J. Lu, and Y.-P. Tan. Discriminative deep metric learning for face verification in the wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1875-1882, 2014. 1, 7
15. J. Hu, J. Lu, J. Yuan, and Y.-P. Tan. Large margin multi-metric learning for face and kinship verification in the wild. In Asian Conference on Computer Vision (ACCV), pages 252-267. 2014. 7
16. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 3
17. T.-K. Kim, O. Arandjelović, and R. Cipolla. Boosted manifold principal angles for image set-based recognition. Pattern Recognition, 40(9):2475-2484, 2007. 2
18. B. F. Klare, B. Klein, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, M. Burge, and A. K. Jain. Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1931-1939, 2015. 2, 4, 5, 6
19. K.-C. Lee, J. Ho, M.-H. Yang, and D. Kriegman. Video-based face recognition using probabilistic appearance manifolds. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003. 2
20. H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang. Probabilistic elastic matching for pose variant face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3499-3506, 2013. 1, 2
21. H. Li, G. Hua, X. Shen, Z. Lin, and J. Brandt. Eigen-PEP for video face recognition. In Asian Conference on Computer Vision (ACCV), pages 17-33. 2014. 1, 2, 7, 8
22. L. Liu, L. Zhang, H. Liu, and S. Yan. Toward large-population face identification in unconstrained videos. IEEE Transactions on Circuits and Systems for Video Technology, 24(11):1874-1884, 2014. 1, 2, 5, 8
23. I. Masi, S. Rawls, G. Medioni, and P. Natarajan. Pose-aware face recognition in the wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4838-4846, 2016. 1, 5, 6
24. I. Masi, A. T. A. Trãn, J. Toy Leksut, T. Hassner, and G. Medioni. Do we really need to collect millions of faces for effective face recognition? In European Conference on Computer Vision (ECCV), 2016. 5, 6
25. H. Mendez-Vazquez, Y. Martinez-Diaz, and Z. Chai. Volume structured ordinal features with background similarity measure for video face recognition. In International Conference on Biometrics (ICB), 2013. 1
26. M. Ngan and P. Grother. Face recognition vendor test (FRVT) performance of automated gender classification algorithms. In Technical Report NIST IR 8052. National Institute of Standards and Technology, 2015. 6
27. O. M. Parkhi, K. Simonyan, A. Vedaldi, and A. Zisserman. A compact and discriminative face track descriptor. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1693-1700, 2014. 1, 2
28. O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In British Machine Vision Conference (BMVC), volume 1, page 6, 2015. 1, 2, 5, 6, 7
29. S. Ren, X. Cao, Y. Wei, and J. Sun. Face alignment at 3000 fps via regressing local binary features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1685-1692, 2014. 5
30. S. Sankaranarayanan, A. Alavi, C. Castillo, and R. Chellappa. Triplet probabilistic embedding for face verification and clustering. arXiv preprint arXiv:1604.05417, 2016. 6
31. F. Schroff, D. Kalenichenko, and J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 815-823, 2015. 1, 2, 5, 7
32. S. Sukhbaatar, J. Weston, R. Fergus, et al. End-to-end memory networks. In Advances in Neural Information Processing Systems (NIPS), pages 2440-2448, 2015. 3
33. Y. Sun, X. Wang, and X. Tang. Deeply learned face representations are sparse, selective, and robust. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2892-2900, 2015. 1, 7
34. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-9, 2015. 3
35. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. DeepFace: Closing the gap to human-level performance in face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1701-1708, 2014. 1, 2, 5, 7
36. T. Tieleman and G. Hinton. RMSProp: Divide the gradient by a running average of its recent magnitude. Technical report, 2012. 5
37. P. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa. Statistical computations on grassmann and stiefel manifolds for image and video-based recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11):2273-2286, 2011. 2
38. O. Vinyals, S. Bengio, and M. Kudlur. Order matters: sequence to sequence for sets. In International Conference on Learning Representation, 2016. 2, 3
39. D. Wang, C. Otto, and A. K. Jain. Face search at scale: 80 million gallery. arXiv preprint arXiv:1507.07242, 2015. 6
40. R. Wang, S. Shan, X. Chen, and W. Gao. Manifold-manifold distance with application to face recognition based on image set. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2008. 2
41. Y. Wen, K. Zhang, Z. Li, and Y. Qiao. A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision (ECCV), pages 499-515, 2016. 1, 7
42. L. Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 529-534, 2011. 1, 2, 5, 7
43. L. Wolf and N. Levy. The SVM-Minus similarity score for video face recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3523-3530, 2013. 1