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38
CITATION.md
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@ -4,7 +4,22 @@ Please cite deepface in your publications if it helps your research. Here are it
### Facial Recognition
If you use deepface in your research for facial recogntion purposes, please cite the this publication.
If you use deepface in your research for facial recogntion purposes, please cite these publications:
```BibTeX
@article{serengil2024lightface,
title = {A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
journal = {Bilişim Teknolojileri Dergisi},
volume = {17},
number = {2},
pages = {X–X},
year = {2024},
doi = {10.17671/gazibtd.XXX},
url = {XXX},
publisher = {Gazi University}
}
```
```BibTeX
@inproceedings{serengil2020lightface,
@ -14,14 +29,14 @@ If you use deepface in your research for facial recogntion purposes, please cite
pages = {23-27},
year = {2020},
doi = {10.1109/ASYU50717.2020.9259802},
url = {https://doi.org/10.1109/ASYU50717.2020.9259802},
url = {https://ieeexplore.ieee.org/document/9259802},
organization = {IEEE}
}
```
### Facial Attribute Analysis
If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction or face detection purposes, please cite the this publication.
If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction, please cite the this publication.
```BibTeX
@inproceedings{serengil2021lightface,
@ -31,11 +46,26 @@ If you use deepface in your research for facial attribute analysis purposes such
pages = {1-4},
year = {2021},
doi = {10.1109/ICEET53442.2021.9659697},
url = {https://doi.org/10.1109/ICEET53442.2021.9659697},
url = {https://ieeexplore.ieee.org/document/9659697/},
organization = {IEEE}
}
```
### Additional Papers
We have additionally released these papers within the DeepFace project for a multitude of purposes.
```BibTeX
@misc{serengil2023db,
title = {An evaluation of sql and nosql databases for facial recognition pipelines},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
year = {2023},
archivePrefix = {Cambridge Open Engage},
doi = {10.33774/coe-2023-18rcn},
url = {https://www.cambridge.org/engage/coe/article-details/63f3e5541d2d184063d4f569}
}
```
### Repositories
Also, if you use deepface in your GitHub projects, please add `deepface` in the `requirements.txt`. Thereafter, your project will be listed in its [dependency graph](https://github.com/serengil/deepface/network/dependents).

58
README.md
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@ -136,20 +136,21 @@ embedding_objs = DeepFace.represent(img_path = "img.jpg",
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-20240316.jpg" width="95%" height="95%"></p>
FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwvhbLdI) ones based on experiments. You can find out the scores of those models below on [Labeled Faces in the Wild](https://sefiks.com/2020/08/27/labeled-faces-in-the-wild-for-face-recognition/) set declared by its creators.
FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments - see [`BENCHMARKS`](https://github.com/serengil/deepface/tree/master/benchmarks) for more details. You can find the measured scores of various models in DeepFace and the reported scores from their original studies in the following table.
| Model | Declared LFW Score |
| -------------- | ------------------ |
| VGG-Face | 98.9% |
| Facenet | 99.2% |
| Facenet512 | 99.6% |
| OpenFace | 92.9% |
| DeepID | 97.4% |
| Dlib | 99.3 % |
| SFace | 99.5% |
| ArcFace | 99.5% |
| GhostFaceNet | 99.7% |
| *Human-beings* | *97.5%* |
| Model | Measured Score | Declared Score |
| -------------- | -------------- | ------------------ |
| Facenet512 | 98.4% | 99.6% |
| Human-beings | 97.5% | 97.5% |
| Facenet | 97.4% | 99.2% |
| Dlib | 96.8% | 99.3 % |
| VGG-Face | 96.7% | 98.9% |
| ArcFace | 96.7% | 99.5% |
| GhostFaceNet | 93.3% | 99.7% |
| SFace | 93.0% | 99.5% |
| OpenFace | 78.7% | 92.9% |
| DeepFace | 69.0% | 97.3% |
| DeepID | 66.5% | 97.4% |
Conducting experiments with those models within DeepFace may reveal disparities compared to the original studies, owing to the adoption of distinct detection or normalization techniques. Furthermore, some models have been released solely with their backbones, lacking pre-trained weights. Thus, we are utilizing their re-implementations instead of the original pre-trained weights.
@ -194,7 +195,7 @@ Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision an
**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/), [`Faster MTCNN`](https://github.com/timesler/facenet-pytorch), [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/), [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/), [`YOLOv8 Face`](https://github.com/derronqi/yolov8-face) and [`YuNet`](https://github.com/ShiqiYu/libfacedetection) detectors are wrapped in deepface.
Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/), [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/), [`Faster MTCNN`](https://github.com/timesler/facenet-pytorch), [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/), [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/), `Yolo` and `YuNet` detectors are wrapped in deepface.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v5.jpg" width="95%" height="95%"></p>
@ -342,7 +343,22 @@ You can also support this work on [Patreon](https://www.patreon.com/serengil?rep
Please cite deepface in your publications if it helps your research - see [`CITATIONS`](https://github.com/serengil/deepface/blob/master/CITATION.md) for more details. Here are its BibTex entries:
If you use deepface in your research for facial recogntion purposes, please cite this publication.
If you use deepface in your research for facial recogntion purposes, please cite these publications:
```BibTeX
@article{serengil2024lightface,
title = {A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules},
author = {Serengil, Sefik Ilkin and Ozpinar, Alper},
journal = {Bilişim Teknolojileri Dergisi},
volume = {17},
number = {2},
pages = {X–X},
year = {2024},
doi = {10.17671/gazibtd.XXX},
url = {XXX},
publisher = {Gazi University}
}
```
```BibTeX
@inproceedings{serengil2020lightface,
@ -352,12 +368,12 @@ If you use deepface in your research for facial recogntion purposes, please cite
pages = {23-27},
year = {2020},
doi = {10.1109/ASYU50717.2020.9259802},
url = {https://doi.org/10.1109/ASYU50717.2020.9259802},
url = {https://ieeexplore.ieee.org/document/9259802},
organization = {IEEE}
}
```
If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction or face detection purposes, please cite this publication.
If you use deepface in your research for facial attribute analysis purposes such as age, gender, emotion or ethnicity prediction, please cite this publication.
```BibTeX
@inproceedings{serengil2021lightface,
@ -367,7 +383,7 @@ If you use deepface in your research for facial attribute analysis purposes such
pages = {1-4},
year = {2021},
doi = {10.1109/ICEET53442.2021.9659697},
url = {https://doi.org/10.1109/ICEET53442.2021.9659697},
url = {https://ieeexplore.ieee.org/document/9659697},
organization = {IEEE}
}
```
@ -378,6 +394,10 @@ Also, if you use deepface in your GitHub projects, please add `deepface` in the
DeepFace is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
DeepFace 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), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. 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 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), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning.
Similarly, DeepFace wraps many face detectors: [OpenCv](https://github.com/opencv/opencv/blob/4.x/LICENSE), [Ssd](https://github.com/opencv/opencv/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/LICENSE.txt), [MtCnn](https://github.com/ipazc/mtcnn/blob/master/LICENSE), [Fast MtCnn](https://github.com/timesler/facenet-pytorch/blob/master/LICENSE.md), [RetinaFace](https://github.com/serengil/retinaface/blob/master/LICENSE), [MediaPipe](https://github.com/google/mediapipe/blob/master/LICENSE), [YuNet](https://github.com/ShiqiYu/libfacedetection/blob/master/LICENSE) and [Yolo](https://github.com/derronqi/yolov8-face/blob/main/LICENSE).
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/).