Difference between revisions of "DeepFace"
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{{Technology | {{Technology | ||
|Developped by (institutions)=Facebook | |Developped by (institutions)=Facebook | ||
| + | |Developped by (people)=Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf | ||
|Creation Date=2014 | |Creation Date=2014 | ||
|Technology Type=Deep learning facial recognition system | |Technology Type=Deep learning facial recognition system | ||
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==Links== | ==Links== | ||
| + | Source for Developped by[[CiteRef::taigmanDeepFaceClosingGap2014]] | ||
| + | |||
[[url::https://pypi.org/project/deepface/|https://pypi.org/project/deepface/]] | [[url::https://pypi.org/project/deepface/|https://pypi.org/project/deepface/]] | ||
Revision as of 23:36, 1 March 2021
Technology Type: Facial Recognition
Details:
| Dataset used | |
|---|---|
| Related Technologies | Alexnet |
| Code Repository |
Developers and Users:
| Developed by | Facebook AI Research |
|---|
Events:
| Creation Date | 2014 |
|---|---|
| End Date |
Description[ ]
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 four 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
Links[edit | ]
Source for Developped by2