Saved searches
Use saved searches to filter your results more quickly
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.
The world’s simplest facial recognition api for Python and the command line
License
mukira/face_recognition
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Name already in use
A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Sign In Required
Please sign in to use Codespaces.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching Xcode
If nothing happens, download Xcode and try again.
Launching Visual Studio Code
Your codespace will open once ready.
There was a problem preparing your codespace, please try again.
Latest commit
Git stats
Files
Failed to load latest commit information.
README.md
You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어.
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)
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 it 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:
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)
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
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.
Installing on an Nvidia Jetson Nano board
- 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.
Installing on Raspberry Pi 2+
While Windows isn’t officially supported, helpful users have posted instructions on how to install this library:
Installing a pre-configured Virtual Machine image
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.
face_recognition command line tool
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.
face_detection command line tool
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).
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,NoneIf 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 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!
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.
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!
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.
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!")
All the examples are available here.
