"Megaface 2: 672,057 Identities for Face Recognition", Aaron Nech, Ira Kemelmacher-Shlizerman, 2016

title={Megaface 2: 672,057 Identities for Face Recognition},
author={Nech, Aaron and Kemelmacher-Shlizerman, Ira},

"The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", Ira Kemelmacher-Shlizerman, Steve Seitz, Daniel Miller, Evan Brossard, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

title={The MegaFace Benchmark: 1 Million Faces for Recognition at Scale},
author={Kemelmacher-Shlizerman, Ira and Seitz, Steven M and Miller, Daniel and Brossard, Evan},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},

Participating Methods

[1] FaceNet: A Unified Embedding for Face Recognition and Clustering, Florian Schroff, Dmitry Kalenichenko, James Philbin, CVPR 2015
[2] Beijing Faceall Co.
[3] NTechLAB
[4] 3DiVi Company
[6] Vocord
[7] Deepsense
[8] Shanghai Tech
[9] Barebones_FR
[10] Joint Bayes implementation
[11] LBP features comparison (no training)


[1] Learned-Miller, Erik, et al. "Labeled faces in the wild: A survey." Advances in Face Detection and Facial Image Analysis. Springer International Publishing, 2016. 189-248.
[2] Ghazi, Mostafa Mehdipour, and Hazim Kemal Ekenel. "A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition." arXiv preprint arXiv:1606.02894 (2016).
[3] Hu, Guosheng, et al. "Frankenstein: Learning Deep Face Representations using Small Data." arXiv preprint arXiv:1603.06470 (2016).
[4] Zhu, Shizhan, et al. "Unconstrained Face Alignment via Cascaded Compositional Learning."
[5] Guo, Yandong, et al. "MS-Celeb-1M: Challenge of Recognizing One Million Celebri-ties in the Real World."
[6] Zhong, Yang, Josephine Sullivan, and Haibo Li. "Face Attribute Prediction Using Off-The-Shelf Deep Learning Networks." arXiv preprint arXiv:1602.03935 (2016).