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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)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

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):

• Python 3.3 or Python 2.7
• macOS or Linux (Windows not officially supported, but might work)

First, make sure you have dlib already installed with Python bindings:

• How to install dlib from source on macOS or Ubuntu

Then, install this module from pypi using pip3 (or 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.

• Jetson Nano installation instructions
  • Please follow the instructions in the article carefully. There is current a bug in the CUDA libraries on the Jetson Nano that will cause this library to fail silently if you don't follow the instructions in the article to comment out a line in dlib and recompile it.

• Raspberry Pi 2 installation instructions

pkg install graphics/py-face_recognition

While Windows isn't officially supported, helpful users have posted instructions on how to install this library:

• @masoudr's Windows 10 installation guide (dlib face_recognition)

• Download the pre-configured VM image (for VMware Player or VirtualBox).

When you install face_recognition, you get two simple command-line programs:

• face_recognition - Recognize faces in a photograph or folder full for photographs.
• face_detection - Find faces in a photograph or folder full for photographs.

The 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.

An unknown_person is a face in the image that didn't match anyone in your folder of known people.

The 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 --show-distance true:

$ 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 when compliling dlib.

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[0]['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)[0]

# 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)[0]

# 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[0] == 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.

• Find faces in a photograph
• Find faces in a photograph (using deep learning)
• Find faces in batches of images w/ GPU (using deep learning)
• Blur all the faces in a live video using your webcam (Requires OpenCV to be installed)

• Identify specific facial features in a photograph
• Apply (horribly ugly) digital make-up

• Find and recognize unknown faces in a photograph based on photographs of known people
• Identify and draw boxes around each person in a photo
• Compare faces by numeric face distance instead of only True/False matches
• Recognize faces in live video using your webcam - Simple / Slower Version (Requires OpenCV to be installed)
• Recognize faces in live video using your webcam - Faster Version (Requires OpenCV to be installed)
• Recognize faces in a video file and write out new video file (Requires OpenCV to be installed)
• Recognize faces on a Raspberry Pi w/ camera
• Run a web service to recognize faces via HTTP (Requires Flask to be installed)
• Recognize faces with a K-nearest neighbors classifier
• Train multiple images per person then recognize faces using a SVM

If you want to create a standalone executable that can run without the need to install python or 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.

• My article on how Face Recognition works: Modern Face Recognition with Deep Learning
  • Covers the algorithms and how they generally work
• Face recognition with OpenCV, Python, and deep learning by Adrian Rosebrock
  • Covers how to use face recognition in practice
• Raspberry Pi Face Recognition by Adrian Rosebrock
  • Covers how to use this on a Raspberry Pi
• Face clustering with Python by Adrian Rosebrock
  • Covers how to automatically cluster photos based on who appears in each photo using unsupervised learning

If you want to learn how face location and recognition work instead of depending on a black box library, read my article.

• The face recognition model is trained on adults and does not work very well on children. It tends to mix up children quite easy using the default comparison threshold of 0.6.
• Accuracy may vary between ethnic groups. Please see this wiki page for more details.

Since 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

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.

• Many, many thanks to Davis King (@nulhom) for creating dlib and for providing the trained facial feature detection and face encoding models used in this library. For more information on the ResNet that powers the face encodings, check out his blog post.
• Thanks to everyone who works on all the awesome Python data science libraries like numpy, scipy, scikit-image, pillow, etc, etc that makes this kind of stuff so easy and fun in Python.
• Thanks to Cookiecutter and the audreyr/cookiecutter-pypackage project template for making Python project packaging way more tolerable.