Difference between revisions of "DeepFace (dataset)"
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<blockquote>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 [[Has images::4000000|4 million]] facial images belonging to more than [[Has individuals::4,000|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%. [[CiteRef::rajiFaceSurveyFacial2021]]</blockquote> | <blockquote>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 [[Has images::4000000|4 million]] facial images belonging to more than [[Has individuals::4,000|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%. [[CiteRef::rajiFaceSurveyFacial2021]]</blockquote> | ||
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This page uses the following references: | This page uses the following references: | ||
Revision as of 17:23, 20 April 2024
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.