Face Recognition

Features

Find faces in pictures

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)

Find and manipulate facial features in pictures

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)

Identify faces in pictures

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.

Installation

Requirements

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

Installing on Mac or Linux

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

Installing on Raspberry Pi 2+

• Raspberry Pi 2+ installation instructions

Installing on Windows

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)

Installing a pre-configured Virtual Machine image

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

Usage

Command-Line Interface

Next, you need a second folder with the files you want to identify:

$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/

/unknown_pictures/unknown.jpg,Barack Obama
/face_recognition_test/unknown_pictures/unknown.jpg,unknown_person

Adjusting Tolerance / Sensitivity

$ 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

$ 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

More Examples

$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2

Barack Obama
unknown_person

Speeding up Face Recognition

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.

Python Module

API Docs: https://face-recognition.readthedocs.io.

Automatically find all the faces in an image

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!

You can also opt-in to a somewhat more accurate deep-learning-based face detection model.

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!

Automatically locate the facial features of a person in an image

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.

Recognize faces in images and identify who they are

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!")

Python Code Examples

All the examples are available here.

Face Detection

• Find faces in a photograph
• Find faces in a photograph (using deep learning)
• Find faces in batches of images w/ GPU (using deep learning)

Facial Features

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

Facial Recognition

• Find and recognize unknown faces in a photograph based on photographs of known people

• 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

  How Face Recognition Works

Caveats

• 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.

Deployment to Cloud Hosts (Heroku, AWS, etc)

Common Issues

Issue: Illegal instruction (core dumped) when using face_recognition or running examples.

Issue: RuntimeError: Unsupported image type, must be 8bit gray or RGB image. when running the webcam examples.

Solution: Your webcam probably isn't set up correctly with OpenCV. Look here for more.

Issue: MemoryError when running pip2 install face_recognition

Issue: AttributeError: 'module' object has no attribute 'face_recognition_model_v1'

Solution: The version of dlib you have installed is too old. You need version 19.7 or newer. Upgrade dlib.

Issue: Attribute Error: 'Module' object has no attribute 'cnn_face_detection_model_v1'

Solution: The version of dlib you have installed is too old. You need version 19.7 or newer. Upgrade dlib.

Issue: TypeError: imread() got an unexpected keyword argument 'mode'

Solution: The version of scipy you have installed is too old. You need version 0.17 or newer. Upgrade scipy.

Thanks

• 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.