the world's simplest face recognition library.
built with deep learning. The model has an accuracy of 99.38% on the
Labeled Faces in the Wild benchmark.
face_recognition
command line tool that letsyou 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)
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.
- 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:
Then, install this module from pypi using pip3
(or pip2
for Python 2):
pip3 install face_recognition
pre-configured VM.
While Windows isn't officially supported, helpful users have posted instructions on how to install this library:
- Download the pre-configured VM image (for VMware Player or VirtualBox).
face_recognition
, you get a simple command-line programcalled
face_recognition
that you can use to recognize faces in aphotograph or folder full for photographs.
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:
face_recognition
, passing inthe 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
with the filename and the name of the person found.
unknown_person
is a face in the image that didn't match anyone inyour folder of known people.
the people in your photos look very similar and a lower tolerance value
is needed to make face comparisons more strict.
--tolerance
parameter. The default tolerancevalue 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
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
care about file names, you could do this:
$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2
Barack Obama
unknown_person
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.
face_recognition
module and then easily manipulatefaces 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!
to try it out.
You can also opt-in to a somewhat more accurate deep-learning-based face detection model.
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.
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.
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!")
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)
- 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
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.
face_recognition
depends on dlib
which is written in C++, it can be tricky to deploy an appusing it to a cloud hosting provider like Heroku or AWS.
face_recognition
in a Docker container. With that, you should be able to deployto any service that supports Docker images.
Issue: Illegal instruction (core dumped)
when using face_recognition or running examples.
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
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.