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README.md
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<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p>
Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/), [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and SFace.
Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
@ -60,7 +60,7 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
**Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI) **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)
Deepface is a **hybrid** face recognition package. It currently wraps many **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). The default configuration uses VGG-Face model. Deepface is a **hybrid** face recognition package. It currently wraps many **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and SFace. The default configuration uses VGG-Face model.
```python ```python
models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"] models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
@ -79,6 +79,7 @@ FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwv
| Model | LFW Score | YTF Score | | Model | LFW Score | YTF Score |
| --- | --- | --- | | --- | --- | --- |
| Facenet512 | 99.65% | - | | Facenet512 | 99.65% | - |
| SFace | 99.60% | - |
| ArcFace | 99.41% | - | | ArcFace | 99.41% | - |
| Dlib | 99.38 % | - | | Dlib | 99.38 % | - |
| Facenet | 99.20% | - | | Facenet | 99.20% | - |
@ -256,6 +257,6 @@ Also, if you use deepface in your GitHub projects, please add deepface in the re
## Licence ## Licence
Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details. However, the library wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE) and [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt). Besides, age, gender and race / ethnicity models are based on VGG-Face. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes. Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details. However, the library wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), and [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE). Besides, age, gender and race / ethnicity models are based on VGG-Face. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.
Deepface [logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/) and it is licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/). Deepface [logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/) and it is licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).

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deepface/commons/distance.py
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@ -32,6 +32,9 @@ def findThreshold(model_name, distance_metric):
'ArcFace': {'cosine': 0.68, 'euclidean': 4.15, 'euclidean_l2': 1.13}, 'ArcFace': {'cosine': 0.68, 'euclidean': 4.15, 'euclidean_l2': 1.13},
'Dlib': {'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.4}, 'Dlib': {'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.4},
#TODO: find the best threshold values
'SFace': {'cosine': 0.40, 'euclidean': 9.5, 'euclidean_l2': 0.90},
'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55}, 'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64}, 'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
'DeepID': {'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17} 'DeepID': {'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17}