Package: face-recognition
Face RecognitionRecognize and manipulate faces from Python or from the command line
withthe world’s simplest face recognition library.Built usingdlib’s state-of-the-art face
recognitionbuilt with deep learning. The model has an accuracy of 99.38% on theLabeled Faces in the Wildbenchmark.This also provides a simpleface_recognitioncommand line tool
that letsyou do face recognition on a folder of images from the command line!FeaturesFind faces in picturesFind all the faces that appear in a picture:importface_recognitionimage=face_recognition.load_image_file("your_file.jpg")face_locations=face_recognition.face_locations(image)Find and manipulate facial features in picturesGet the locations and outlines of each person’s eyes, nose, mouth and
chin.importface_recognitionimage=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 stufflike applyingdigital
make-up(think ‘Meitu’):Identify faces in picturesRecognize who appears in each photo.importface_recognitionknown_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:Seethis
examplefor the code.InstallationRequirementsPython 3.3+ or Python 2.7macOS or Linux (Windows not officially supported, but might work)Installing on Mac or LinuxFirst, make sure you have dlib already installed with Python bindings:How to install dlib from source on macOS or
UbuntuThen, install this module from pypi usingpip3(orpip2for
Python 2):pip3installface_recognitionIf you are having trouble with installation, you can also try out apre-configured
VM.Installing on Raspberry Pi 2+Raspberry Pi 2+ installation
instructionsInstalling on WindowsWhile 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 imageDownload the pre-configured VM
image(for VMware Player or VirtualBox).UsageCommand-Line InterfaceWhen you installface_recognition, you get a simple command-line
programcalledface_recognitionthat you can use to recognize faces in aphotograph or folder full for photographs.First, you need to provide a folder with one picture of each person
youalready know. There should be one image file for each person with thefiles 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 commandface_recognition, passing inthe folder of known people and the folder (or single image) with
unknownpeople and it tells you who is in each image:$face_recognition./pictures_of_people_i_know/./unknown_pictures//unknown_pictures/unknown.jpg,BarackObama/face_recognition_test/unknown_pictures/unknown.jpg,unknown_personThere’s one line in the output for each face. The data is
comma-separatedwith the filename and the name of the person found.Anunknown_personis a face in the image that didn’t match anyone
inyour folder of known people.Adjusting Tolerance / SensitivityIf you are getting multiple matches for the same person, it might be
thatthe people in your photos look very similar and a lower tolerance
valueis needed to make face comparisons more strict.You can do that with the--toleranceparameter. The default
tolerancevalue is 0.6 and lower numbers make face comparisons more strict:$face_recognition--tolerance0.54./pictures_of_people_i_know/./unknown_pictures//unknown_pictures/unknown.jpg,BarackObama/face_recognition_test/unknown_pictures/unknown.jpg,unknown_personIf you want to see the face distance calculated for each match in
orderto adjust the tolerance setting, you can use--show-distancetrue:$face_recognition--show-distancetrue./pictures_of_people_i_know/./unknown_pictures//unknown_pictures/unknown.jpg,BarackObama,0.378542298956785/face_recognition_test/unknown_pictures/unknown.jpg,unknown_person,NoneMore ExamplesIf you simply want to know the names of the people in each photograph
but don’tcare about file names, you could do this:$face_recognition./pictures_of_people_i_know/./unknown_pictures/|cut-d','-f2BarackObamaunknown_personSpeeding up Face RecognitionFace recognition can be done in parallel if you have a computer withmultiple CPU cores. For example if your system has 4 CPU cores, you
canprocess about 4 times as many images in the same amount of time by
usingall 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--cpus4./pictures_of_people_i_know/./unknown_pictures/You can also pass in--cpus-1to use all CPU cores in your system.Python ModuleYou can import theface_recognitionmodule and then easily
manipulatefaces 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 imageimportface_recognitionimage=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!Seethis
exampleto 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
goodperformance with this model. You’ll also want to enable CUDA supportwhen complilingdlib.importface_recognitionimage=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!Seethis
exampleto try it out.If you have a lot of images and a GPU, you can alsofind faces in
batches.Automatically locate the facial features of a person in an imageimportface_recognitionimage=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.Seethis
exampleto try it out.Recognize faces in images and identify who they areimportface_recognitionpicture_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)ifresults[0]==True:print("It's a picture of me!")else:print("It's not a picture of me!")Seethis
exampleto try it out.Python Code ExamplesAll the examples are availablehere.Face DetectionFind faces in a
photographFind faces in a photograph (using deep
learning)Find faces in batches of images w/ GPU (using deep
learning)Facial FeaturesIdentify specific facial features in a
photographApply (horribly ugly) digital
make-upFacial RecognitionFind and recognize unknown faces in a photograph based on
photographs of known
peopleCompare faces by numeric face distance instead of only True/False
matchesRecognize 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/
cameraRun a web service to recognize faces via HTTP (Requires Flask to be
installed)Recognize faces with a K-nearest neighbors
classifierHow Face Recognition WorksIf you want to learn how face location and recognition work instead ofdepending on a black box library,read my
article.CaveatsThe 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)Sinceface_recognitiondepends ondlibwhich is written in
C++, it can be tricky to deploy an appusing 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 withface_recognitionin aDockercontainer. With that, you should be able to deployto any service that supports Docker images.Common IssuesIssue:Illegal instruction (core dumped)when using
face_recognition or running examples.Solution:dlibis compiled with SSE4 or AVX support, but your CPU
is too old and doesn’t support that.You’ll need to recompiledlibaftermaking 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:MemoryErrorwhen runningpip2 install face_recognitionSolution: The face_recognition_models file is too big for your
available pip cache memory. Instead,trypip2--no-cache-dirinstall face_recognitionto avoid the
issue.Issue:AttributeError: 'module' object has no attribute 'face_recognition_model_v1'Solution: The version ofdlibyou have installed is too old. You
need version 19.7 or newer. Upgradedlib.Issue:Attribute Error: 'Module' object has no attribute 'cnn_face_detection_model_v1'Solution: The version ofdlibyou have installed is too old. You
need version 19.7 or newer. Upgradedlib.Issue:TypeError: imread() got an unexpected keyword argument 'mode'Solution: The version ofscipyyou have installed is too old. You
need version 0.17 or newer. Upgradescipy.ThanksMany, many thanks toDavis 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
hisblog
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 toCookiecutterand theaudreyr/cookiecutter-pypackageproject template
for making Python project packaging way more tolerable.History1.2.3 (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 3.7New example of using this library in a Jupyter Notebook1.2.2 (2018-04-02)Added the face_detection CLI commandRemoved dependencies on scipy to make installation easierCleaned up KNN example and fixed a bug with drawing fonts to label detected faces in the demo1.2.1 (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 conversionUpdated webcam examples to avoid common mistakes and reduce support questionsAdded a KNN classification exampleAdded an example of automatically blurring faces in images or videosUpdated 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 versionface_recognition_models v0.3.0 is now the minimum required version1.0.0 (2017-08-29)Added support for dlib’s CNN face detection model via model=”cnn” parameter on face detecion callAdded support for GPU batched face detections using dlib’s CNN face detector modelAdded find_faces_in_picture_cnn.py to examplesAdded find_faces_in_batches.py to examplesAdded face_rec_from_video_file.py to examplesdlib v19.5 is now the minimum required versionface_recognition_models v0.2.0 is now the minimum required version0.2.2 (2017-07-07)Added –show-distance to cliFixed a bug where –tolerance was ignored in cli if testing a single imageAdded benchmark.py to examples0.2.1 (2017-07-03)Added –tolerance to cli0.2.0 (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 exampleImproved CLI tests to actually test the CLI functionalityUpdated 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 2.70.1.13 (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.