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Recognize faces from Python or from the command line

author Adam Geitgey
author_email ageitgey@gmail.com
  • Development Status :: 4 - Beta
  • Intended Audience :: Developers
  • License :: OSI Approved :: MIT License
  • Natural Language :: English
  • Programming Language :: Python :: 2
  • Programming Language :: Python :: 2.7
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3.5
  • Programming Language :: Python :: 3.6
  • Programming Language :: Python :: 3.7
  • Programming Language :: Python :: 3.8
keywords face_recognition
license MIT license
  • face-recognition-models (>=0.3.0)
  • Click (>=6.0)
  • dlib (>=19.7)
  • numpy
  • Pillow

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Face Recognition

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!
Build Status
Documentation Status


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)
Finding facial features is super useful for lots of important stuff. But you can also use for really stupid stuff
like applying digital make-up (think ‘Meitu’):


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.



  • 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:

Then, install this module from pypi using pip3 (or pip2 for Python 2):

pip3 install face_recognition
If you are having trouble with installation, you can also try out a

Installing on Raspberry Pi 2+

Installing on Windows

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

Installing a pre-configured Virtual Machine image


Command-Line Interface

When you install face_recognition, you get a simple command-line program
called face_recognition that you can use to 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
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.
Adjusting Tolerance / Sensitivity
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
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
More Examples
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
Speeding up Face Recognition
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.

Python Module

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.

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!
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!
to try it out.
If you have a lot of images and a GPU, you can also
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.
to try it out.
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!")
    print("It's not a picture of me!")
to try it out.


  • 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)

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.

Common Issues

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

Solution: dlib is compiled with SSE4 or AVX support, but your CPU is too old and doesn’t support that.
You’ll need to recompile dlib after making the code change outlined here.

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

Solution: The face_recognition_models file is too big for your available pip cache memory. Instead,
try pip2 --no-cache-dir install face_recognition to avoid the issue.

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.


  • 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.


1.2.3 (2018-08-21)

  • You can now pass model=”small” to face_landmarks() to use the 5-point face model instead of the 68-point model.
  • Now officially supporting Python 3.7
  • New example of using this library in a Jupyter Notebook

1.2.2 (2018-04-02)

  • Added the face_detection CLI command
  • Removed dependencies on scipy to make installation easier
  • Cleaned up KNN example and fixed a bug with drawing fonts to label detected faces in the demo

1.2.1 (2018-02-01)

  • Fixed version numbering inside of module code.

1.2.0 (2018-02-01)

  • Fixed a bug where batch size parameter didn’t work correctly when doing batch face detections on GPU.
  • Updated OpenCV examples to do proper BGR -> RGB conversion
  • Updated webcam examples to avoid common mistakes and reduce support questions
  • Added a KNN classification example
  • Added an example of automatically blurring faces in images or videos
  • Updated Dockerfile example to use dlib v19.9 which removes the boost dependency.

1.1.0 (2017-09-23)

  • Will use dlib’s 5-point face pose estimator when possible for speed (instead of 68-point face pose esimator)
  • dlib v19.7 is now the minimum required version
  • face_recognition_models v0.3.0 is now the minimum required version

1.0.0 (2017-08-29)

  • Added support for dlib’s CNN face detection model via model=”cnn” parameter on face detecion call
  • Added support for GPU batched face detections using dlib’s CNN face detector model
  • Added find_faces_in_picture_cnn.py to examples
  • Added find_faces_in_batches.py to examples
  • Added face_rec_from_video_file.py to examples
  • dlib v19.5 is now the minimum required version
  • face_recognition_models v0.2.0 is now the minimum required version

0.2.2 (2017-07-07)

  • Added –show-distance to cli
  • Fixed a bug where –tolerance was ignored in cli if testing a single image
  • Added benchmark.py to examples

0.2.1 (2017-07-03)

  • Added –tolerance to cli

0.2.0 (2017-06-03)

  • The CLI can now take advantage of multiple CPUs. Just pass in the -cpus X parameter where X is the number of CPUs to use.
  • Added face_distance.py example
  • Improved CLI tests to actually test the CLI functionality
  • Updated facerec_on_raspberry_pi.py to capture in rgb (not bgr) format.

0.1.14 (2017-04-22)

  • Fixed a ValueError crash when using the CLI on Python 2.7

0.1.13 (2017-04-20)

  • Raspberry Pi support.

0.1.12 (2017-04-13)

  • Fixed: Face landmarks wasn’t returning all chin points.

0.1.11 (2017-03-30)

  • Fixed a minor bug in the command-line interface.

0.1.10 (2017-03-21)

  • Minor pref improvements with face comparisons.
  • Test updates.

0.1.9 (2017-03-16)

  • Fix minimum scipy version required.

0.1.8 (2017-03-16)

  • Fix missing Pillow dependency.

0.1.7 (2017-03-13)

  • First working release.