base on A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python # deepface <div align="center"> [](https://pepy.tech/project/deepface) [](https://github.com/serengil/deepface/stargazers) [](https://hub.docker.com/r/serengil/deepface) [](https://github.com/serengil/deepface/blob/master/LICENSE) [](https://github.com/serengil/deepface/actions/workflows/tests.yml) [](https://doi.org/10.17671/gazibtd.1399077) [](https://sefiks.com) [](https://www.youtube.com/@sefiks?sub_confirmation=1) [](https://twitter.com/intent/user?screen_name=serengil) [](https://www.patreon.com/serengil?repo=deepface) [](https://github.com/sponsors/serengil) [](https://buymeacoffee.com/serengil) <div align="center"> <a href="https://trendshift.io/repositories/4227" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4227" alt="serengil%2Fdeepface | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div> </div> <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/), [`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/), [`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/), `SFace`, `GhostFaceNet`, `Buffalo_L`. [A modern face recognition pipeline](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) consists of 5 common stages: [detect](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [normalize](https://sefiks.com/2020/11/20/facial-landmarks-for-face-recognition-with-dlib/), [represent](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and [verify](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). While DeepFace handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code. [`Experiments`](https://github.com/serengil/deepface/tree/master/benchmarks) show that **human beings have 97.53% accuracy** on facial recognition tasks whereas those models already reached and passed that accuracy level. ## Installation [](https://pypi.org/project/deepface/) The easiest way to install deepface is to download it from [`PyPI`](https://pypi.org/project/deepface/). It's going to install the library itself and its prerequisites as well. ```shell $ pip install deepface ``` Alternatively, you can also install deepface from its source code. Source code may have new features not published in pip release yet. ```shell $ git clone https://github.com/serengil/deepface.git $ cd deepface $ pip install -e . ``` Once you installed the library, then you will be able to import it and use its functionalities. ```python from deepface import DeepFace ``` **Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE) This function determines whether two facial images belong to the same person or to different individuals. It accepts exact image file paths as input, but also supports NumPy arrays, base64-encoded images, and URLs. The function returns a dictionary, where the key of interest is `verified`: True indicates the images are of the same person, while False means they are of different people. In addition to this binary classification, the function also provides a [`confidence`](https://youtu.be/QQ4vO6UOsFo) score that reflects the likelihood that the two images represent the same person. ```python result: dict = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/verify-credit.jpg" width="99%"></p> **Face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s) [Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. DeepFace provides an out-of-the-box `find` function that searches for the identity of an input image within a specified database path. It returns a list of pandas DataFrames containing the results. Meanwhile, facial embeddings are stored in a pickle file to be searched faster in next time. ```python dfs: List[pd.DataFrame] = DeepFace.find(img_path = "img1.jpg", db_path = "C:/my_db") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%"></p> Here, the `find` function relies on a directory-based face datastore and stores embeddings on disk. Alternatively, DeepFace provides a database-backed `search` functionality where embeddings are explicitly registered and queried. Currently, postgres, mongo and weaviate are supported as backend databases. ```python # register an image into the database DeepFace.register(img = "img1.jpg") # perform exact search dfs = DeepFace.search(img = "target.jpg") ``` If you want to perform [`approximate nearest neighbor`](https://sefiks.com/2023/12/31/a-step-by-step-approximate-nearest-neighbor-example-in-python-from-scratch/) search instead of exact search to achieve faster results on [large-scale databases](https://www.youtube.com/playlist?list=PLsS_1RYmYQQGSJu_Z3OVhXhGmZ86_zuIm), you can build an index beforehand and explicitly enable ANN search. Here, [Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) is used to index embeddings in postgres and mongo whereas weaviate handles indexing internally. ```python # build index on registered embeddings (for postgres and mongo only) DeepFace.build_index() # perform approximate nearest neighbor search dfs = DeepFace.search(img = "target.jpg", search_method = "ann") ``` For database-backed search, exact search is suitable for datasets up to ~10k entries, typically returning results in less than a second; Postgres or Mongo with ANN works well for datasets from ~10k to 1M entries, with typical response times of seconds; and Vector databases such as Weaviate optimized for very large-scale datasets from ~1M to billions entries (and can scale further with clustering), typically returning results in seconds. **Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA) DeepFace also comes with a strong facial attribute analysis module including [`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/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Result is going to be the size of faces appearing in the source image. ```python objs: List[dict] = DeepFace.analyze( img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'] ) ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-2.jpg" width="95%"></p> Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its [tutorial](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/). **Real Time Analysis** - [`Demo`](https://youtu.be/-c9sSJcx6wI), [`React Demo part-i`](https://youtu.be/IXoah6rhxac), [`React Demo part-ii`](https://youtu.be/_waBA-cH2D4) You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequentially 5 frames. Then, it shows results 5 seconds. ```python DeepFace.stream(db_path = "C:/database") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-3.jpg" width="90%"></p> Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below. ```bash user ├── database │ ├── Alice │ │ ├── Alice1.jpg │ │ ├── Alice2.jpg │ ├── Bob │ │ ├── Bob.jpg ``` If you intend to perform face verification or analysis tasks directly from your browser, [`deepface-react-ui`](https://github.com/serengil/deepface-react-ui) is a separate repository built using ReactJS depending on deepface api. Here, you can also find some real time demos for various facial recognition models: <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/icon/deepface-realtime.jpg" width="90%"></p> | Task | Model | Demo | | --- | --- | --- | | Facial Recognition | DeepFace | [`Video`](https://youtu.be/YjYIMs5ZOfc) | | Facial Recognition | FaceNet | [`Video`](https://youtu.be/vB1I5vWgTQg) | | Facial Recognition | VGG-Face | [`Video`](https://youtu.be/tSU_lNi0gQQ) | | Facial Recognition | OpenFace | [`Video`](https://youtu.be/-4z2sL6wzP8) | | Age & Gender | Default | [`Video`](https://youtu.be/tFI7vZn3P7E) | | Race & Ethnicity | Default | [`Video`](https://youtu.be/-ztiy5eJha8) | | Emotion | Default | [`Video`](https://youtu.be/Y7DfLvLKScs) | | Celebrity Look-Alike | Default | [`Video`](https://youtu.be/RMgIKU1H8DY) | **Embeddings** - [`Tutorial`](https://sefiks.com/2025/06/28/what-are-vector-embeddings-and-why-they-matter-in-ai/), [`Demo`](https://youtu.be/OYialFo7Qo4) Face recognition models basically represent facial images as multi-dimensional vectors. Sometimes, you need those embedding vectors directly. DeepFace comes with a dedicated representation function. Represent function returns a list of embeddings. Result is going to be the size of faces appearing in the image path. ```python embedding_objs = DeepFace.represent(img_path = "img.jpg") ``` Embeddings can be [plotted](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) as below. Each slot is corresponding to a dimension value and dimension value is emphasized with colors. Similar to 2D barcodes, vertical dimension stores no information in the illustration. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%"></p> In summary, the distance between vector embeddings of the same person should be smaller than that between embeddings of different people. When reduced to two-dimensional space, the clusters become clearly distinguishable. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/icon/facenet-pca.png" width="95%"></p> **Face recognition models** - [`Demo`](https://youtu.be/eKOZawGR3y0) 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/) , [`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/), [`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/), `SFace`, `GhostFaceNet` and `Buffalo_L`. The default configuration uses VGG-Face model. ```python models = [ "VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", "GhostFaceNet", "Buffalo_L", ] result = DeepFace.verify( img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[0] ) dfs = DeepFace.find( img_path = "img1.jpg", db_path = "C:/my_db", model_name = models[1] ) embeddings = DeepFace.represent( img_path = "img.jpg", model_name = models[2] ) ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-20240316.jpg" width="95%"></p> 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 | 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. **Face Detection and Alignment** - [`Demo`](https://youtu.be/GZ2p2hj2H5k) Face detection and alignment are important early stages of a modern face recognition pipeline. [Experiments](https://github.com/serengil/deepface/tree/master/benchmarks) show that detection increases the face recognition accuracy up to 42%, while alignment increases it up to 6%. [`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`, [`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`, `YuNet` and `CenterFace` detectors are wrapped in deepface. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v6.jpg" width="95%"></p> All deepface functions accept optional detector backend and align input arguments. You can switch among those detectors and alignment modes with these arguments. OpenCV is the default detector and alignment is on by default. ```python backends = [ 'opencv', 'ssd', 'dlib', 'mtcnn', 'fastmtcnn', 'retinaface', 'mediapipe', 'yolov8n', 'yolov8m', 'yolov8l', 'yolov11n', 'yolov11s', 'yolov11m', 'yolov11l', 'yolov12n', 'yolov12s', 'yolov12m', 'yolov12l', 'yunet', 'centerface', ] detector = backends[3] align = True obj = DeepFace.verify( img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = detector, align = align ) dfs = DeepFace.find( img_path = "img.jpg", db_path = "my_db", detector_backend = detector, align = align ) embedding_objs = DeepFace.represent( img_path = "img.jpg", detector_backend = detector, align = align ) demographies = DeepFace.analyze( img_path = "img4.jpg", detector_backend = detector, align = align ) face_objs = DeepFace.extract_faces( img_path = "img.jpg", detector_backend = detector, align = align ) ``` Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-outputs-20240414.jpg" width="90%"></p> [RetinaFace](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/) and [MtCnn](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn. The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/retinaface-results.jpeg" width="90%"> <br><em>The Yellow Angels - Fenerbahce Women's Volleyball Team</em> </p> You can find out more about RetinaFace on this [repo](https://github.com/serengil/retinaface). **Face Anti Spoofing** - [`Demo`](https://youtu.be/UiK1aIjOBlQ) DeepFace also includes an anti-spoofing analysis module to understand given image is real or fake. To activate this feature, set the `anti_spoofing` argument to True in any DeepFace tasks. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/face-anti-spoofing.jpg" width="40%"></p> ```python # anti spoofing test in face detection face_objs = DeepFace.extract_faces(img_path="dataset/img1.jpg", anti_spoofing = True) assert all(face_obj["is_real"] is True for face_obj in face_objs) # anti spoofing test in real time analysis DeepFace.stream(db_path = "C:/database", anti_spoofing = True) ``` **Similarity** - [`Demo`](https://youtu.be/1EPoS69fHOc) Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent faces as vectors. We expect that a face pair of same person should be [more similar](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/) than a face pair of different persons. Similarity could be calculated by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Angular Distance, Euclidean Distance or L2 normalized Euclidean. The default configuration uses cosine similarity. According to [experiments](https://github.com/serengil/deepface/tree/master/benchmarks), no distance metric is overperforming than other. ```python metrics = ["cosine", "euclidean", "euclidean_l2", "angular"] result = DeepFace.verify( img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1] ) dfs = DeepFace.find( img_path = "img1.jpg", db_path = "C:/my_db", distance_metric = metrics[2] ) ``` **API** - [`Demo`](https://youtu.be/HeKCQ6U9XmI), [`Docker Demo`](https://youtu.be/9Tk9lRQareA) DeepFace serves an API as well - see [`api folder`](https://github.com/serengil/deepface/tree/master/deepface/api/src) for more details. You can clone deepface source code and run the api with the following command. It will use gunicorn server to get a rest service up. In this way, you can call deepface from an external system such as mobile app or web. ```shell cd scripts && ./service.sh ``` Alternatively, you can run the dockerized service. ```shell cd scripts && ./dockerize.sh ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-api.jpg" width="90%"></p> Face verification, facial attribute analysis, vector representation and register & search functions are covered in the API. The API accepts images as file uploads (via form data), or as exact image paths, URLs, or base64-encoded strings (via either JSON or form data). ```shell $ curl -X POST http://localhost:5005/represent -d '{"model_name":"Facenet", "img":"img1.jpg"}' -H "Content-Type: application/json" $ curl -X POST http://localhost:5005/verify -d '{"img1":"img1.jpg", "img2":"img3.jpg"}' -H "Content-Type: application/json" $ curl -X POST http://localhost:5005/analyze -d '{"img": "img2.jpg", "actions": ["age", "gender"]}' -H "Content-Type: application/json" $ curl -X POST http://localhost:5005/register -d '{"model_name":"Facenet", "img":"img18.jpg"}' -H "Content-Type: application/json" $ curl -X POST http://localhost:5005/search -d '{"img":"img1.jpg", "model_name":"Facenet"}' -H "Content-Type: application/json" ``` [`Here`](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called. **Encrypt Embeddings** - [`Demo with PHE`](https://youtu.be/8VCu39jFZ7k), [`Tutorial for PHE`](https://sefiks.com/2025/03/04/vector-similarity-search-with-partially-homomorphic-encryption-in-python/), [`Demo with FHE`](https://youtu.be/njjw0PEhH00), [`Tutorial for FHE`](https://sefiks.com/2021/12/01/homomorphic-facial-recognition-with-tenseal/) Vector embeddings, though not reversible, carry sensitive information like fingerprints, making their security crucial. Encrypting them prevents adversarial misuse. Traditional encryption (e.g., AES) is secure but unsuitable for cloud-based distance calculations. [Homomorphic encryption](https://youtu.be/3ejI0zNPMEQ) allows computations on encrypted data without revealing content—ideal for secure cloud processing. For example, the cloud can compute encrypted similarity without knowing the data, while only the key holder can decrypt the result. See the [`LightPHE`](https://github.com/serengil/LightPHE) library for partially homomorphic encryption. ```python from lightphe import LightPHE # build an additively homomorphic cryptosystem (e.g. Paillier) on-prem cs = LightPHE(algorithm_name = "Paillier", precision = 19) # define encrypted and plain vectors encrypted_alpha = DeepFace.represent("source.jpg", cryptosystem=cs)[0]["encrypted_embedding"] beta = DeepFace.represent("target.jpg")[0]["embedding"] # dot product of encrypted & plain embedding in cloud - private key not required encrypted_cosine_similarity = encrypted_alpha @ beta # decrypt similarity on-prem - private key required calculated_similarity = cs.decrypt(encrypted_cosine_similarity)[0] # verification print("same person" if calculated_similarity >= 1 - threshold else "different persons") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/icon/encrypt-embeddings.jpg" width="60%"></p> For stronger privacy, fully homomorphic encryption enables dot product computations between encrypted embeddings, but it's far more computationally intensive. Explore [`CipherFace`](https://github.com/serengil/cipherface) for FHE-based approaches. ### Extended Applications DeepFace can also be used for fun and insightful applications such as **Find Your Celebrity Look-Alike** - [`Demo`](https://youtu.be/jaxkEn-Kieo), [`Real-Time Demo`](https://youtu.be/RMgIKU1H8DY), [`Tutorial`](https://sefiks.com/2019/05/05/celebrity-look-alike-face-recognition-with-deep-learning-in-keras/) DeepFace can analyze your facial features and match them with celebrities, letting you discover which famous personality you resemble the most. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/celebrity-look-alike.jpg" width="55%"></p> **Find Which Parent a Child Look More** - [`Demo`](https://youtu.be/nza4tmi9vhE), [`Tutorial`](https://sefiks.com/2022/12/22/decide-whom-your-child-looks-like-with-facial-recognition-mommy-or-daddy/) DeepFace can also be used to compare a child's face to their parents' or relatives' faces to determine which one the child resembles more. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/parental-look-alike-scaled.jpg" width="90%"></p> ## Contribution Pull requests are more than welcome! If you are planning to contribute a large patch, please create an issue first to get any upfront questions or design decisions out of the way first. Before creating a PR, you should run the unit tests and linting locally by running `make test && make lint` command. Once a PR sent, GitHub test workflow will be run automatically and unit test and linting jobs will be available in [GitHub actions](https://github.com/serengil/deepface/actions) before approval. ## Support There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏 It really helps the project get discovered by more people. If you do like this work, then you can support it financially on [Patreon](https://www.patreon.com/serengil?repo=deepface), [GitHub Sponsors](https://github.com/sponsors/serengil) or [Buy Me a Coffee](https://buymeacoffee.com/serengil). <a href="https://www.patreon.com/serengil?repo=deepface"> <img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%"> </a> ## Citation Please cite deepface in your publications if it helps your research. <details open> <summary>S. Serengil and A. Ozpinar, <b>"A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules"</b>, <i>Journal of Information Technologies</i>, vol. 17, no. 2, pp. 95-107, 2024.</summary> ```BibTeX @article{serengil2024lightface, title = {A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules}, author = {Serengil, Sefik and Ozpinar, Alper}, journal = {Journal of Information Technologies}, volume = {17}, number = {2}, pages = {95-107}, year = {2024}, doi = {10.17671/gazibtd.1399077}, url = {https://dergipark.org.tr/en/pub/gazibtd/issue/84331/1399077}, publisher = {Gazi University} } ``` </details> <details> <summary>S. I. Serengil and A. Ozpinar, <b>"HyperExtended LightFace: A Facial Attribute Analysis Framework"</b>, <i>2021 International Conference on Engineering and Emerging Technologies (ICEET)</i>, 2021, pp. 1-4.</summary> ```BibTeX @inproceedings{serengil2021lightface, title = {HyperExtended LightFace: A Facial Attribute Analysis Framework}, author = {Serengil, Sefik Ilkin and Ozpinar, Alper}, booktitle = {2021 International Conference on Engineering and Emerging Technologies (ICEET)}, pages = {1-4}, year = {2021}, doi = {10.1109/ICEET53442.2021.9659697}, url = {https://ieeexplore.ieee.org/document/9659697}, organization = {IEEE} } ``` </details> <details> <summary>S. I. Serengil and A. Ozpinar, <b>"LightFace: A Hybrid Deep Face Recognition Framework"</b>, <i>2020 Innovations in Intelligent Systems and Applications Conference (ASYU)</i>, 2020, pp. 23-27.</summary> ```BibTeX @inproceedings{serengil2020lightface, title = {LightFace: A Hybrid Deep Face Recognition Framework}, author = {Serengil, Sefik Ilkin and Ozpinar, Alper}, booktitle = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)}, pages = {23-27}, year = {2020}, doi = {10.1109/ASYU50717.2020.9259802}, url = {https://ieeexplore.ieee.org/document/9259802}, organization = {IEEE} } ``` </details> Also, if you use deepface in your GitHub projects, please add `deepface` in the `requirements.txt`. ## Licence 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) (both 128d and 512d), [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), [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE) and [Buffalo_L](https://github.com/deepinsight/insightface/blob/master/README.md). 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), [Yolo](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [CenterFace](https://github.com/Star-Clouds/CenterFace/blob/master/LICENSE). Finally, DeepFace is optionally using [face anti spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/blob/master/LICENSE) to determine the given images are real or fake. License types will be inherited when you intend to utilize 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/). ", Assign "at most 3 tags" to the expected json: {"id":"4227","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"