The MegaFace benchmark: 1 million faces for recognition at scale

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 4873-4882

  Kemelmacher Shlizerman, Ira ^a   Seitz, Steven M ^a   Miller, Daniel ^a   Brossard, Evan ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; FACE RECOGNITION;

FACE RECOGNITION EXPERIMENT; HUMAN RECOGNITION; PERFORMANCE OF ALGORITHM; STATE OF THE ART; TRAINING DATA;

PATTERN RECOGNITION;

EID: 84986292192     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.527     Document Type: Conference Paper

References (37)

1. T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(12):2037-2041, 2006.
2. L. Best-Rowden, H. Han, C. Otto, B. Klare, and A. K. Jain. Unconstrained face recognition: Identifying a person of interest from a media collection. 2014.
3. J. R. Beveridge, P. J. Phillips, D. S. Bolme, B. A. 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 Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on, pages 1-8. IEEE, 2013.
4. B. Bhattarai, G. Sharma, F. Jurie, and P. Pérez. Some faces are more equal than others: Hierarchical organization for accurate and efficient large-scale identity-based face retrieval. In Computer Vision-ECCV 2014 Workshops, pages 160-172. Springer, 2014.
5. B.-C. Chen, C.-S. Chen, and W. H. Hsu. Cross-age reference coding for age-invariant face recognition and retrieval. In Proceedings of the European Conference on Computer Vision (ECCV), 2014.
6. D. Chen, X. Cao, L.Wang, F.Wen, and J. Sun. Bayesian face revisited: A joint formulation. In Computer Vision-ECCV 2012, pages 566-579. Springer, 2012.
7. T. Cootes and A. Lanitis. The fg-net aging database, 2008.
8. B. DeCann and A. Ross. Can a poor verification system be a good identification system? a preliminary study. In Information Forensics and Security (WIFS), 2012 IEEE International Workshop on, pages 31-36. IEEE, 2012.
9. E. Eidinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. Information Forensics and Security, IEEE Transactions on, 9(12):2170-2179, 2014.
10. A. Georghiades. Yale face database. Center for computational Vision and Control at Yale University, http://cvc. yale. edu/projects/yalefaces/yalefa, 1997.
11. R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-pie. Image and Vision Computing, 28(5):807-813, 2010.
12. P. Grother and P. J. Phillips. Models of large population recognition performance. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II-68. IEEE, 2004.
13. P. J. Grother, G. W. Quinn, and P. J. Phillips. Report on the evaluation of 2d still-image face recognition algorithms. NIST interagency report, 7709:106, 2010.
14. G. Hu, Y. Yang, D. Yi, J. Kittler, W. Christmas, S. Z. Li, and T. Hospedales. When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. arXiv preprint arXiv:1504.02351, 2015.
15. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07-49, University of Massachusetts, Amherst, 2007.
16. I. Kemelmacher-Shlizerman, S. Suwajanakorn, and S. M. Seitz. Illumination-aware age progression. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 3334-3341. IEEE, 2014.
17. J. C. Klontz and A. K. Jain. A case study on unconstrained facial recognition using the boston marathon bombings suspects. Michigan State University, Tech. Rep, 119:120, 2013.
18. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and simile classifiers for face verification. In Computer Vision, 2009 IEEE 12th International Conference on, pages 365-372. IEEE, 2009.
19. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Describable visual attributes for face verification and image search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(10):1962-1977, 2011.
20. Z. Li, U. Park, and A. K. Jain. A discriminative model for age invariant face recognition. Information Forensics and Security, IEEE Transactions on, 6(3):1028-1037, 2011.
21. M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool. Face detection without bells and whistles. In ECCV, 2014.
22. H.-W. Ng and S. Winkler. A data-driven approach to cleaning large face datasets. people, 265(265):530.
23. E. G. Ortiz and B. C. Becker. Face recognition for webscale datasets. Computer Vision and Image Understanding, 118:153-170, 2014.
24. O. M. Parkhi, A. Vedaldi, A. Zisserman, A. Vedaldi, K. Lenc, M. Jaderberg, K. Simonyan, A. Vedaldi, A. Zisserman, K. Lenc, et al. Deep face recognition. Proceedings of the British Machine Vision, 2015.
25. F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. arXiv preprint arXiv:1503.03832, 2015.
26. Z. Stone, T. Zickler, and T. Darrell. Autotagging facebook: Social network context improves photo annotation. In Computer Vision and Pattern Recognition Workshops, 2008. CVPRW'08. IEEE Computer Society Conference on, pages 1-8. IEEE, 2008.
27. Y. Sun, D. Liang, X. Wang, and X. Tang. Deepid3: Face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873, 2015.
28. Y. Sun, X. Wang, and X. Tang. Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265, 2014.
29. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701-1708. IEEE, 2014.
30. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Webscale training for face identification. arXiv preprint arXiv:1406.5266, 2014.
31. B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li. The new data and new challenges in multimedia research. arXiv preprint arXiv:1503.01817, 2015.
32. P. Viola and M. J. Jones. Robust real-time face detection. International journal of computer vision, 57(2):137-154, 2004.
33. D. Wang, C. Otto, and A. K. Jain. Face search at scale: 80 million gallery. arXiv preprint arXiv:1507.07242, 2015.
34. L.Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 529-534. IEEE, 2011.
35. X. Xiong and F. De la Torre. Supervised descent method and its applications to face alignment. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 532-539. IEEE, 2013.
36. D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014.
37. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. Acm Computing Surveys (CSUR), 35(4):399-458, 2003.