Difference between revisions of "DeepFace (dataset)"
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|Developed by Institution=Facebook | |Developed by Institution=Facebook | ||
|Creation Date=2014 | |Creation Date=2014 | ||
| − | |Dataset Category=Facial | + | |Dataset Category=Facial Images |
|URL=https://pypi.org/project/deepface/ | |URL=https://pypi.org/project/deepface/ | ||
|Related Technology=DeepFace | |Related Technology=DeepFace | ||
Revision as of 16:36, 12 March 2021
Technical information:
| Full name | |
|---|---|
| Country | |
| Contents | Facial Images |
| Images | 4,000,000 |
| Individuals | 4,000 |
| Runs database software | |
| URL"URL" is a type and predefined property provided by Semantic MediaWiki to represent URI/URL values. | https://pypi.org/project/deepface/ |
| Related Technology | DeepFace |
Developers and Users:
| Developed by | |
|---|---|
| Owning institution | |
| Custodian institution |
Description[edit | ]
It was not until the breakthrough of Alexnet in 2012, and the subsequent introduction of the DeepFace model in 2014, that the use of neural networks became a mainstream method for facial recognition development. DeepFace, the first facial recognition model trained with deep learning, was also the first instance of a facial recognition model approaching human performance on a task. Deepface was developed by researchers at Facebook, Inc. and trained on an internal dataset composed of images from Facebook profile images; at the time, it was purportedly “the largest facial dataset to-date, an identity labeled dataset of 4 million facial images belonging to more than 4,000 identities” (Taigman et al. 2014).The impact of deep learning techniques on face recognition and its adjacent problems was dramatic; the DeepFace model achieved a 97.35% accuracy on the Labeled Faces in the Wild (LfW) test set, reducing the previous state of the art’s error by 27%. 1
References
- ^ Raji, Inioluwa Deborah and Fried, Genevieve. About Face: A Survey of Facial Recognition Evaluation. , 2021.