|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Face_recognition||47,583||45||5 days ago||21||February 20, 2020||707||mit||Python|
|The world's simplest facial recognition api for Python and the command line|
|Easyocr||17,272||37||7 days ago||30||June 02, 2022||183||apache-2.0||Python|
|Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.|
|Computervision Recipes||8,817||2 months ago||65||mit||Jupyter Notebook|
|Best Practices, code samples, and documentation for Computer Vision.|
|Jetson Inference||6,215||2 days ago||929||mit||C++|
|Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.|
|Deepface||5,803||3||5 days ago||74||May 10, 2022||5||mit||Python|
|A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python|
|Lstm Human Activity Recognition||3,074||4 months ago||19||mit||Jupyter Notebook|
|Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier|
|Eagleeye||3,045||a year ago||18||wtfpl||Python|
|Stalk your Friends. Find their Instagram, FB and Twitter Profiles using Image Recognition and Reverse Image Search.|
|Crnn||1,670||4 years ago||85||mit||Lua|
|Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.|
|Anago||1,428||5||a year ago||14||July 17, 2018||43||mit||Python|
|Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.|
|Training_extensions||1,052||20 hours ago||31||apache-2.0||Python|
|Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™|
You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語.
Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library.
Built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.
This also provides a simple
face_recognition command line tool that lets
you do face recognition on a folder of images from the command line!
Find all the faces that appear in a picture:
import face_recognition image = face_recognition.load_image_file("your_file.jpg") face_locations = face_recognition.face_locations(image)
Get the locations and outlines of each person's eyes, nose, mouth and chin.
import face_recognition image = face_recognition.load_image_file("your_file.jpg") face_landmarks_list = face_recognition.face_landmarks(image)
Finding facial features is super useful for lots of important stuff. But you can also use it for really stupid stuff like applying digital make-up (think 'Meitu'):
Recognize who appears in each photo.
import face_recognition known_image = face_recognition.load_image_file("biden.jpg") unknown_image = face_recognition.load_image_file("unknown.jpg") biden_encoding = face_recognition.face_encodings(known_image) unknown_encoding = face_recognition.face_encodings(unknown_image) results = face_recognition.compare_faces([biden_encoding], unknown_encoding)
You can even use this library with other Python libraries to do real-time face recognition:
See this example for the code.
User-contributed shared Jupyter notebook demo (not officially supported):
First, make sure you have dlib already installed with Python bindings:
Then, make sure you have cmake installed:
brew install cmake
Finally, install this module from pypi using
pip2 for Python 2):
pip3 install face_recognition
Alternatively, you can try this library with Docker, see this section.
If you are having trouble with installation, you can also try out a pre-configured VM.
pkg install graphics/py-face_recognition
While Windows isn't officially supported, helpful users have posted instructions on how to install this library:
When you install
face_recognition, you get two simple command-line
face_recognition- Recognize faces in a photograph or folder full for photographs.
face_detection- Find faces in a photograph or folder full for photographs.
face_recognitioncommand line tool
face_recognition command lets you recognize faces in a photograph or
folder full for photographs.
First, you need to provide a folder with one picture of each person you already know. There should be one image file for each person with the files named according to who is in the picture:
Next, you need a second folder with the files you want to identify:
Then in you simply run the command
face_recognition, passing in
the folder of known people and the folder (or single image) with unknown
people and it tells you who is in each image:
$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person
There's one line in the output for each face. The data is comma-separated with the filename and the name of the person found.
unknown_person is a face in the image that didn't match anyone in
your folder of known people.
face_detectioncommand line tool
face_detection command lets you find the location (pixel coordinatates)
of any faces in an image.
Just run the command
face_detection, passing in a folder of images
to check (or a single image):
$ face_detection ./folder_with_pictures/ examples/image1.jpg,65,215,169,112 examples/image2.jpg,62,394,211,244 examples/image2.jpg,95,941,244,792
It prints one line for each face that was detected. The coordinates reported are the top, right, bottom and left coordinates of the face (in pixels).
If you are getting multiple matches for the same person, it might be that the people in your photos look very similar and a lower tolerance value is needed to make face comparisons more strict.
You can do that with the
--tolerance parameter. The default tolerance
value is 0.6 and lower numbers make face comparisons more strict:
$ face_recognition --tolerance 0.54 ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person
If you want to see the face distance calculated for each match in order
to adjust the tolerance setting, you can use
$ face_recognition --show-distance true ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama,0.378542298956785 /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person,None
If you simply want to know the names of the people in each photograph but don't care about file names, you could do this:
$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2 Barack Obama unknown_person
Face recognition can be done in parallel if you have a computer with multiple CPU cores. For example, if your system has 4 CPU cores, you can process about 4 times as many images in the same amount of time by using all your CPU cores in parallel.
If you are using Python 3.4 or newer, pass in a
--cpus <number_of_cpu_cores_to_use> parameter:
$ face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/
You can also pass in
--cpus -1 to use all CPU cores in your system.
You can import the
face_recognition module and then easily manipulate
faces with just a couple of lines of code. It's super easy!
API Docs: https://face-recognition.readthedocs.io.
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_locations = face_recognition.face_locations(image) # face_locations is now an array listing the co-ordinates of each face!
See this example to try it out.
You can also opt-in to a somewhat more accurate deep-learning-based face detection model.
Note: GPU acceleration (via NVidia's CUDA library) is required for good
performance with this model. You'll also want to enable CUDA support
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_locations = face_recognition.face_locations(image, model="cnn") # face_locations is now an array listing the co-ordinates of each face!
See this example to try it out.
If you have a lot of images and a GPU, you can also find faces in batches.
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_landmarks_list = face_recognition.face_landmarks(image) # face_landmarks_list is now an array with the locations of each facial feature in each face. # face_landmarks_list['left_eye'] would be the location and outline of the first person's left eye.
See this example to try it out.
import face_recognition picture_of_me = face_recognition.load_image_file("me.jpg") my_face_encoding = face_recognition.face_encodings(picture_of_me) # my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face! unknown_picture = face_recognition.load_image_file("unknown.jpg") unknown_face_encoding = face_recognition.face_encodings(unknown_picture) # Now we can see the two face encodings are of the same person with `compare_faces`! results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding) if results == True: print("It's a picture of me!") else: print("It's not a picture of me!")
See this example to try it out.
All the examples are available here.
If you want to create a standalone executable that can run without the need to install
face_recognition, you can use PyInstaller. However, it requires some custom configuration to work with this library. See this issue for how to do it.
If you want to learn how face location and recognition work instead of depending on a black box library, read my article.
face_recognition depends on
dlib which is written in C++, it can be tricky to deploy an app
using it to a cloud hosting provider like Heroku or AWS.
To make things easier, there's an example Dockerfile in this repo that shows how to run an app built with
face_recognition in a Docker container. With that, you should be able to deploy
to any service that supports Docker images.
You can try the Docker image locally by running:
docker-compose up --build
There are also several prebuilt Docker images.
Linux users with a GPU (drivers >= 384.81) and Nvidia-Docker installed can run the example on the GPU: Open the docker-compose.yml file and uncomment the
dockerfile: Dockerfile.gpu and
runtime: nvidia lines.
If you run into problems, please read the Common Errors section of the wiki before filing a github issue.