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150 lines
5.6 KiB
Markdown
150 lines
5.6 KiB
Markdown
# Face Recognition Datasets
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## Training Datasets (Updating)
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### CASIA-Webface (10K ids/0.5M images) [1]
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[Baidu](https://pan.baidu.com/s/1AfHdPsxJZBD8kBJeIhmq1w)
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[GDrive](https://drive.google.com/file/d/1KxNCrXzln0lal3N4JiYl9cFOIhT78y1l/view?usp=sharing)
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### CelebA (10K ids/0.2M images) [2]
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### UMDFace (8K ids/0.37M images) [3]
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[Baidu](https://pan.baidu.com/s/1aGutJwNWpV-lA0f_7eNsGQ)
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[GDrive](https://drive.google.com/file/d/1azhEHoJjVmifuzBVKJwl-sDbLZ-Wzp4O/view?usp=sharing)
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### VGG2 (9K ids/3.31M images) [4]
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[Baidu](https://pan.baidu.com/s/1c3KeLzy)
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[GDrive](https://drive.google.com/file/d/1dyVQ7X3d28eAcjV3s3o0MT-HyODp_v3R/view?usp=sharing)
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### MS1M-IBUG (85K ids/3.8M images) [5,6]
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[Baidu](https://pan.baidu.com/s/1nxmSCch)
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### MS1M-ArcFace (85K ids/5.8M images) [5,7]
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[Baidu](https://pan.baidu.com/s/1S6LJZGdqcZRle1vlcMzHOQ)
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[GDrive](https://drive.google.com/file/d/1SXS4-Am3bsKSK615qbYdbA_FMVh3sAvR/view?usp=sharing)
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### MS1M-RetinaFace
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[Baidu](https://pan.baidu.com/s/1RBnaW88PC6cKqtYwgfVX8Q) (code:8eb3)
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[GDrive](https://drive.google.com/file/d/1JgmzL9OLTqDAZE86pBgETtSQL4USKTFy/view?usp=sharing)
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### Asian-Celeb (94K ids/2.8M images)[8]
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[Baidu](https://pan.baidu.com/s/12wSgofDy1flFf6lOyAxJRg)
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### Glint360K (360K ids/17M images)[17]
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[Baidu](https://pan.baidu.com/s/1GsYqTTt7_Dn8BfxxsLFN0w) (code:o3az)
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magnet uri: `magnet:?xt=urn:btih:E5F46EE502B9E76DA8CC3A0E4F7C17E4000C7B1E&dn=glint360k`
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### Glint-Mini (91K ids/5.2M images)[17]
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[Baidu](https://pan.baidu.com/s/10IzEyP-Z9dWFcxxj9jdJpQ) (code:10m5)
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### DeepGlint (181K ids/6.75M images) [8]
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[baidu](https://pan.baidu.com/s/1yApUbklBgRgOyOV4o3J8Eg)
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### WebFace260M [18]
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[Link](https://www.face-benchmark.org/download.html)
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### IMDB-Face (59K ids/1.7M images) [9]
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### Celeb500k (500K ids/50M images) [10]
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### MegaFace(train) (672K ids/4.7M images) [11]
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[Baidu](https://pan.baidu.com/s/1uy366DjUiGc3AvhuamRLyw) (code:5f8m)
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[GDrive](https://drive.google.com/file/d/1O4FxijSXoEIe6fLfOocqF4VFMh5B4d89/view?usp=sharing)
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### DigiFace-1M (110K ids/1.22M images) [19]
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[Website](https://microsoft.github.io/DigiFace1M/)
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[Github](https://github.com/microsoft/DigiFace1M)
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## Validation Datasets
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### CFP-FP (500 ids/7K images/7K pairs)[12]
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### AgeDB-30 (570 ids/12,240 images/6K pairs)[13,6]
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### LFW (5749 ids/13233 images/6K pairs)[14]
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### CALFW (5749 ids/13233 images/6K pairs)[15]
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### CPLFW (5749 ids/13233 images/6K pairs)[16]
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## Image Test Datasets
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### MegaFace
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testsuite: [GDrive](https://drive.google.com/file/d/1KBwp0U9oZgZj7SYDXRxUnnH7Lwvd9XMy/view?usp=sharing)
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### IJB (IJB-B, IJB-C)
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testsuite: [GDrive](https://drive.google.com/file/d/1aC4zf2Bn0xCVH_ZtEuQipR2JvRb1bf8o/view?usp=sharing)
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### TrillionPairs
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### NIST
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## Video Test Datasets
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### YTF
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### IQIYI
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## Reference
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[1] Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li. Learning Face Representation from Scratch. arXiv:1411.7923, 2014.
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[2] Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang. Deep Learning Face Attributes in the Wild, ICCV, 2015.
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[3] Bansal Ankan, Nanduri Anirudh, Castillo Carlos D, Ranjan Rajeev, Chellappa, Rama. UMDFaces: An Annotated Face Dataset for Training Deep Networks, arXiv:1611.01484v2, 2016.
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[4] Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, Andrew Zisserman. VGGFace2: A dataset for recognising faces across pose and age. FG, 2018.
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[5] Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, Jianfeng Gao. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. ECCV, 2016.
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[6] Jiankang Deng, Yuxiang Zhou, Stefanos Zafeiriou. Marginal loss for deep face recognition, CVPRW, 2017.
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[7] Jiankang Deng, Jia Guo, Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition, arXiv:1801.07698, 2018.
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[8] [http://trillionpairs.deepglint.com/](http://trillionpairs.deepglint.com/)
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[9] Wang Fei, Chen Liren, Li Cheng, Huang Shiyao, Chen Yanjie, Qian Chen, Loy, Chen Change. The Devil of Face Recognition is in the Noise, ECCV, 2018.
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[10] Cao Jiajiong, Li Yingming, Zhang Zhongfei, Celeb-500K: A Large Training Dataset for Face Recognition, ICIP, 2018.
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[11] Nech Aaron, Kemelmacher-Shlizerman Ira, Level Playing Field For Million Scale Face Recognition, CVPR, 2017.
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[12] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W,
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Frontal to profile face verification in the wild, WACV, 2016.
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[13] Moschoglou, Stylianos and Papaioannou, Athanasios and Sagonas, Christos and Deng, Jiankang and Kotsia, Irene and Zafeiriou, Stefanos, Agedb: the first manually collected, in-the-wild age database, CVPRW, 2017.
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[14] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.
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Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007.
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[15] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017.
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[16] Zheng, Tianyue, and Weihong Deng. Cross-Pose LFW: A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments, 2018.
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[17] An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and Zhang, Debing and Fu Ying. Partial FC: Training 10 Million Identities on a Single Machine, arxiv:2010.05222, 2020.
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[18] Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou. WebFace260M: A Benchmark Unveiling the Power of Million-scale Deep Face Recognition
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[19] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen. DigiFace-1M: 1 Million Digital Face Images for Face Recognition. WACV 2023 |