Package: nvface
deepfaceDeepface is a lightweightface recognitionand facial attribute analysis (age,gender,emotionandrace) framework for python. It is a hybrid face recognition framework wrappingstate-of-the-artmodels:VGG-Face,Google FaceNet,OpenFace,Facebook DeepFace,DeepID,ArcFaceandDlib.Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.InstallationThe easiest way to install deepface is to download it fromPyPI. It's going to install the library itself and its prerequisites as well. The library is mainly based on TensorFlow and Keras.pipinstalldeepfaceThen you will be able to import the library and use its functionalities.fromdeepfaceimportDeepFaceFacial Recognition-DemoA modernface recognition pipelineconsists of 5 common stages:detect,align,normalize,representandverify. Deepface handles all these common stages in the background. You can just call its verification, find or analysis function with a single line of code.Face Verification-DemoThis function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome.result=DeepFace.verify(img1_path="img1.jpg",img2_path="img2.jpg")Face recognition-DemoFace recognitionrequires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.df=DeepFace.find(img_path="img1.jpg",db_path="C:/workspace/my_db")Face recognition models-DemoDeepface is ahybridface recognition package. It currently wraps manystate-of-the-artface recognition models:VGG-Face,Google FaceNet,OpenFace,Facebook DeepFace,DeepID,ArcFaceandDlib. The default configuration uses VGG-Face model.models=["VGG-Face","Facenet","Facenet512","OpenFace","DeepFace","DeepID","ArcFace","Dlib"]result=DeepFace.verify(img1_path="img1.jpg",img2_path="img2.jpg",model_name=models[1])df=DeepFace.find(img_path="img1.jpg",db_path="C:/workspace/my_db",model_name=models[1])FaceNet, VGG-Face, ArcFace and Dliboverperformsthan OpenFace, DeepFace and DeepID based on experiments. Supportively, FaceNet /w 512d got 99.65%; FaceNet /w 128d got 99.2%; ArcFace got 99.41%; Dlib got 99.38%; VGG-Face got 98.78%; DeepID got 97.05; OpenFace got 93.80% accuracy scores onLFW data setwhereas human beings could have just 97.53%.SimilarityFace recognition models are regularconvolutional neural networksand they are responsible to represent faces as vectors. We expect that a face pair of same person should bemore similarthan a face pair of different persons.Similarity could be calculated by different metrics such asCosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.metrics=["cosine","euclidean","euclidean_l2"]result=DeepFace.verify(img1_path="img1.jpg",img2_path="img2.jpg",distance_metric=metrics[1])df=DeepFace.find(img_path="img1.jpg",db_path="C:/workspace/my_db",distance_metric=metrics[1])Euclidean L2 formseemsto be more stable than cosine and regular Euclidean distance based on experiments.Facial Attribute Analysis-DemoDeepface also comes with a strong facial attribute analysis module includingage,gender,facial expression(including angry, fear, neutral, sad, disgust, happy and surprise) andrace(including asian, white, middle eastern, indian, latino and black) predictions.obj=DeepFace.analyze(img_path="img4.jpg",actions=['age','gender','race','emotion'])Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in itstutorial.Streaming and Real Time Analysis-DemoYou 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 sequantially 5 frames. Then, it shows results 5 seconds.DeepFace.stream(db_path="C:/User/Sefik/Desktop/database")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.user
├──database
│├──Alice
││├──Alice1.jpg
││├──Alice2.jpg
│├──Bob
││├──Bob.jpgFace Detectors-DemoFace detection and alignment are early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%.OpenCV,SSD,Dlib,MTCNNandRetinaFacedetectors are wrapped in deepface. OpenCV is the default detector.backends=['opencv','ssd','dlib','mtcnn','retinaface']#face detection and alignmentdetected_face=DeepFace.detectFace(img_path="img.jpg",detector_backend=backends[4])#face verificationobj=DeepFace.verify(img1_path="img1.jpg",img2_path="img2.jpg",detector_backend=backends[4])#face recognitiondf=DeepFace.find(img_path="img.jpg",db_path="my_db",detector_backend=backends[4])#facial analysisdemography=DeepFace.analyze(img_path="img4.jpg",detector_backend=backends[4])RetinaFaceandMTCNNseem to overperform in detection and alignment stages but they are slower than others. 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.API-DemoDeepface serves an API as well. You can clone/api/api.pyand pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.python api.pyFace recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will behttp://127.0.0.1:5000/verifyfor face recognition,http://127.0.0.1:5000/analyzefor facial attribute analysis, andhttp://127.0.0.1:5000/representfor vector representation. You should pass input images as base64 encoded string in this case.Here, you can find a postman project.Tech Stack-Vlog,TutorialFace recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.embedding=DeepFace.represent(img_path="img.jpg",model_name='Facenet')Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.ContributionPull requests are welcome. You should run the unit tests locally by runningtest/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.SupportThere are many ways to support a project - starring⭐️ the GitHub repo is just one.CitationPlease cite deepface in your publications if it helps your research. Here are examples of its BibTeX entries:@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://doi.org/10.1109/ASYU50717.2020.9259802},organization={IEEE}}@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)},year={2021},organization={IEEE}}Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.LicenceDeepface is licensed under the MIT License - seeLICENSEfor more details. However, the library wraps some external face recognition models:VGG-Face,Facenet,OpenFace,DeepFace,DeepID,ArcFaceandDlib. 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.Deepfacelogois created byAdrien Coquetand it is licensed underCreative Commons: By Attribution 3.0 License.