How To Install Face Recognition In Windows 10
Project description
Confront 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-fine art face up recognition
built with deep learning. The model has an accuracy of 99.38% on the
This also provides a simple
face_recognitioncommand 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 announced in a picture:
import face_recognition paradigm = 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, oral cavity 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 apply for really stupid stuff
Place faces in pictures
Recognize who appears in each photograph.
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 exercise real-time face recognition:
See this case for the code.
Installation
Requirements
- Python iii.iii+ or Python 2.7
- macOS or Linux (Windows not officially supported, but might work)
Installing on Mac or Linux
Showtime, make sure you take dlib already installed with Python bindings:
- How to install dlib from source on macOS or Ubuntu
And then, install this module from pypi using pip3 (or pip2 for Python 2):
pip3 install face_recognition
If y'all are having trouble with installation, you can too effort out a
Usage
Command-Line Interface
When yous install
face_recognition, you lot get a simple command-line program
called
face_recognitionthat you can use to recognize faces in a
photograph or folder full for photographs.
First, you lot need to provide a folder with one movie of each person you
already know. In that location should be one image file for each person with the
files named co-ordinate to who is in the picture show:
Next, y'all need a second binder with the files yous want to identify:
Then in you lot simply run the command
face_recognition, passing in
the binder of known people and the binder (or unmarried epitome) with unknown
people and it tells you who is in each paradigm:
$ 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'south one line in the output for each face up. The data is comma-separated
with the filename and the name of the person found.
An
unknown_personis a confront in the image that didn't match anyone in
your folder of known people.
Adjusting Tolerance / Sensitivity
If yous are getting multiple matches for the same person, it might exist that
the people in your photos look very like and a lower tolerance value
is needed to make face up comparisons more strict.
You tin can do that with the
--toleranceparameter. 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 y'all want to see the face distance calculated for each friction match in order
to conform the tolerance setting, you can employ
--show-altitude 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 lot simply want to know the names of the people in each photo merely don't
care about file names, y'all could exercise this:
$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cutting -d ',' -f2 Barack Obama unknown_person
Speeding up Face Recognition
Face recognition can be done in parallel if y'all have a computer with
multiple CPU cores. For example if your system has 4 CPU cores, you tin
process about iv times as many images in the aforementioned corporeality of time by using
all your CPU cores in parallel.
If yous are using Python iii.iv or newer, pass in a --cpus <number_of_cpu_cores_to_use> parameter:
$ face_recognition --cpus four ./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_recognitionmodule and and then hands dispense
faces with but a couple of lines of code. It'southward super easy!
API Docs: https://face-recognition.readthedocs.io.
Automatically discover all the faces in an image
import face_recognition prototype = face_recognition . load_image_file ( "my_picture.jpg" ) face_locations = face_recognition . face_locations ( image ) # face_locations is now an assortment list the co-ordinates of each face up!
You tin too 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
import face_recognition epitome = 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, y'all can also
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 ( prototype ) # face_landmarks_list is now an array with the locations of each facial characteristic in each face. # face_landmarks_list[0]['left_eye'] would be the location and outline of the outset person'south left centre.
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 exist 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 ] # At present we can come across 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 show of me!" )
Python Lawmaking Examples
All the examples are available here.
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_recognitiondepends on
dlibwhich is written in C++, it can exist tricky to deploy an app
using it to a cloud hosting provider like Heroku or AWS.
To make things easier, there'south an example Dockerfile in this repo that shows how to run an app congenital with
in a Docker container. With that, y'all should exist able to deploy
to whatsoever service that supports Docker images.
Common Issues
Upshot: Illegal instruction (core dumped) when using face_recognition or running examples.
Solution:
dlibis compiled with SSE4 or AVX support, but your CPU is too onetime and doesn't support that.
Consequence: RuntimeError: Unsupported prototype 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 hither for more than.
Outcome: MemoryError when running pip2 install face_recognition
Solution: The face_recognition_models file is too large for your available pip cache retention. Instead,
try
pip2 --no-enshroud-dir install face_recognitionto avoid the outcome.
Upshot: AttributeError: 'module' object has no attribute 'face_recognition_model_v1'
Solution: The version of dlib you have installed is besides old. You demand version 19.vii or newer. Upgrade dlib.
Event: Attribute Mistake: 'Module' object has no aspect 'cnn_face_detection_model_v1'
Solution: The version of dlib you accept installed is likewise onetime. You lot need version 19.7 or newer. Upgrade dlib.
Consequence: TypeError: imread() got an unexpected keyword argument 'style'
Solution: The version of scipy you accept installed is too old. You need version 0.17 or newer. Upgrade scipy.
Thank you
- Many, many thank you to Davis Rex (@nulhom) for creating dlib and for providing the trained facial feature detection and confront encoding models used in this library. For more information on the ResNet that powers the face encodings, cheque out his web log mail.
- Thanks to everyone who works on all the crawly Python data science libraries like numpy, scipy, scikit-image, pillow, etc, etc that makes this kind of stuff and then easy and fun in Python.
- Thanks to Cookiecutter and the audreyr/cookiecutter-pypackage project template for making Python projection packaging fashion more tolerable.
History
1.ii.iii (2018-08-21)
- You can now pass model="small" to face_landmarks() to use the v-point face model instead of the 68-point model.
- Now officially supporting Python three.7
- New instance of using this library in a Jupyter Notebook
1.2.two (2018-04-02)
- Added the face_detection CLI command
- Removed dependencies on scipy to make installation easier
- Cleaned up KNN example and fixed a problems with drawing fonts to label detected faces in the demo
i.two.one (2018-02-01)
- Fixed version numbering inside of module lawmaking.
1.2.0 (2018-02-01)
- Fixed a problems where batch size parameter didn't piece of 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 back up questions
- Added a KNN classification example
- Added an case of automatically blurring faces in images or videos
- Updated Dockerfile example to utilize dlib v19.9 which removes the boost dependency.
1.1.0 (2017-09-23)
- Will use dlib's 5-point face pose reckoner when possible for speed (instead of 68-betoken face up 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 up detection model via model="cnn" parameter on confront detecion phone call
- Added back up 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.five 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 –prove-altitude to cli
- Fixed a bug where –tolerance was ignored in cli if testing a unmarried 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. Only 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.fourteen (2017-04-22)
- Fixed a ValueError crash when using the CLI on Python 2.7
0.ane.xiii (2017-04-twenty)
- Raspberry Pi support.
0.one.12 (2017-04-xiii)
- Fixed: Face landmarks wasn't returning all chin points.
0.ane.11 (2017-03-30)
- Fixed a modest issues in the command-line interface.
0.1.ten (2017-03-21)
- Minor pref improvements with face up comparisons.
- Exam updates.
0.i.9 (2017-03-sixteen)
- Ready minimum scipy version required.
0.1.eight (2017-03-16)
- Prepare missing Pillow dependency.
0.1.seven (2017-03-xiii)
- First working release.
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Source Distribution
Congenital Distribution
Source: https://pypi.org/project/face-recognition/
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