Recognize faces from Python or from the command line
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) unknown_encoding = face_recognition.face_encodings(unknown_image) 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+ or Python 2.7
- macOS or Linux (Windows untested)
- Also can run on a Raspberry Pi 2+ (follow these specific instructions)
- A pre-configured VM image is also available.
Install this module from pypi using pip3 (or pip2 for Python 2):
pip3 install face_recognition
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
$ 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.
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['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) # 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) # 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 == 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.
- 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)
How Face Recognition Works
- 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)
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 19.4 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.
- 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
- Added –show-distance to cli
- Fixed a bug where –tolerance was ignored in cli if testing a single image
- Added benchmark.py to examples
- Added –tolerance to cli
- 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.
- Fixed a ValueError crash when using the CLI on Python 2.7
- Raspberry Pi support.
- Fixed: Face landmarks wasn’t returning all chin points.
- Fixed a minor bug in the command-line interface.
- Minor pref improvements with face comparisons.
- Test updates.
- Fix minimum scipy version required.
- Fix missing Pillow dependency.
- First working release.
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