face-recognition 1.3.0

Recognize faces from Python or from the command line

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!

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)

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.

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

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

pre-configured VM.

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

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
/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.

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
/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

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
unknown_person

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!

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.

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.

See this example

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!")
else:
    print("It's not a picture of me!")

See this example

to try it out.

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

If you want to learn how face location and recognition work instead of

depending on a black box library, read my article.

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)

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.

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.

History

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.