![]() Result is going to be the size of faces appearing in the image path. Represent function returns a list of embeddings. DeepFace comes with a dedicated representation function. Sometimes, you need those embedding vectors directly. find ( img_path = "img1.jpg", db_path = "C:/workspace/my_db" )įace recognition models basically represent facial images as multi-dimensional vectors. Besides, target images in the database can have many faces as well. Result is going to be the size of faces appearing in the source image. Meanwhile, facial embeddings of the facial database are stored in a pickle file to be searched faster in next time. It's going to look for the identity of input image in the database path and it will return list of pandas data frame as output. Herein, deepface has an out-of-the-box find function to handle this action. In this case, the most similar faces will be compared.įace recognition requires applying face verification many times. Verification function can also handle many faces in the face pairs. verify ( img1_path = "img1.jpg", img2_path = "img2.jpg" ) Then, it is going to return a dictionary and you should check just its verified key. Passing numpy or base64 encoded images is also welcome. This function verifies face pairs as same person or different persons. You can just call its verification, find or analysis function with a single line of code. 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. from deepface import DeepFaceĪ modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. ![]() Then you will be able to import the library and use its functionalities. Thirdly, you can install deepface from its source code. You can alternatively install the package via conda. Secondly, DeepFace is also available at Conda. It's going to install the library itself and its prerequisites as well. The easiest way to install deepface is to download it from PyPI. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace.Įxperiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. Deepface is a lightweight face recognition and facial attribute analysis ( age, gender, emotion and race) framework for python. ![]()
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