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Face Recognition Login

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Latest version

Released:

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Recognize faces from Python or from the command line

Project description

Face Recognition

Recognize and manipulate faces from Python or from the command linewith
Built using dlib's state-of-the-art facerecognition
built with deep learning. The model has an accuracy of 99.38% on the
This also provides a simple face_recognition command line toolthat lets
you do face recognition on a folder of images from the command line!

Features

Find faces in pictures

Find all the faces that appear in a picture:

Find and manipulate facial features in pictures

Get the locations and outlines of each person's eyes, nose, mouth andchin.

Finding facial features is super useful for lots of important stuff.But you can also use for really stupid stuff

Identify faces in pictures

Recognize who appears in each photo.

You can even use this library with other Python libraries to doreal-time face recognition:

See thisexamplefor the code.

Installation

Requirements

  • 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 forPython 2):

If you are having trouble with installation, you can also try out a

Installing on Raspberry Pi 2+

Installing on Windows

While Windows isn't officially supported, helpful users have postedinstructions on how to install this library:

Installing a pre-configured Virtual Machine image

  • Download the pre-configured VMimage(for VMware Player or VirtualBox).

Usage

Command-Line Interface

When you install face_recognition, you get a simple command-lineprogram
called face_recognition that you can use to recognize faces in a
First, you need to provide a folder with one picture of each personyou
already know. There should be one image file for each person with the

Next, you need a second folder with the files you want to identify:

Install Windows Hello Windows 10

Then in you simply run the command face_recognition, passing in
the folder of known people and the folder (or single image) withunknown
There's one line in the output for each face. The data iscomma-separated
with the filename and the name of the person found.
An unknown_person is a face in the image that didn't match anyonein
Adjusting Tolerance / Sensitivity
If you are getting multiple matches for the same person, it might bethat
the people in your photos look very similar and a lower tolerancevalue
You can do that with the --tolerance parameter. The defaulttolerance
value is 0.6 and lower numbers make face comparisons more strict:
If you want to see the face distance calculated for each match inorder
to adjust the tolerance setting, you can use --show-distance true:
More Examples
If you simply want to know the names of the people in each photographbut don't
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, youcan
process about 4 times as many images in the same amount of time byusing

If you are using Python 3.4 or newer, pass in a--cpus parameter:

You can also pass in --cpus-1 to use all CPU cores in your system.

Python Module

You can import the face_recognition module and then easilymanipulate
faces 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 image
to try it out.

Quickbooks using license number. You can also opt-in to a somewhat more accurate deep-learning-based facedetection model.

Note: GPU acceleration (via nvidia's CUDA library) is required forgood
performance with this model. You'll also want to enable CUDA support
to try it out.
If you have a lot of images and a GPU, you can also
Automatically locate the facial features of a person in an image
to try it out.
Recognize faces in images and identify who they are
to try it out.

Python Code Examples

All the examples are availablehere.

Face Detection

Facial Features

Facial Recognition

  • How Face Recognition Works

If you want to learn how face location and recognition work instead of

Caveats

  • The face recognition model is trained on adults and does not workvery well on children. It tends to mixup children quite easy using the default comparison threshold of 0.6.

Deployment to Cloud Hosts (Heroku, AWS, etc)

Since face_recognition depends on dlib which is written inC++, it can be tricky to deploy an app
using it to a cloud hosting provider like Heroku or AWS.
To make things easier, there's an example Dockerfile in this repo thatshows how to run an app built with
face_recognition in a Dockercontainer. With that, you should be able to deploy

Face Recognition Login Software

Common Issues

Issue: Illegal instruction (core dumped) when usingface_recognition or running examples.

Solution: dlib is compiled with SSE4 or AVX support, but your CPUis too old and doesn't support that.
Face
Recognition
You'll need to recompile dlib after making the code changeoutlinedhere.

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. Lookhere formore.

Issue: MemoryError when running pip2 install face_recognition

Solution: The face_recognition_models file is too big for youravailable pip cache memory. Instead,
try pip2 --no-cache-dir install face_recognition to avoid theissue.

Issue:AttributeError: 'module' object has no attribute 'face_recognition_model_v1'

Solution: The version of dlib you have installed is too old. Youneed 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. Youneed version 19.7 or newer. Upgrade dlib.

Issue: TypeError: imread() got an unexpected keyword argument 'mode'

Solution: The version of scipy https://bestofiles412.weebly.com/a-better-finder-rename-11-12-5.html. you have installed is too old. Youneed version 0.17 or newer. Upgrade scipy.

Thanks

  • Many, many thanks to Davis King(@nulhom)for creating dlib and for providing the trained facial featuredetection and face encoding modelsused in this library. For more information on the ResNet that powersthe face encodings, check outhis blogpost.
  • Thanks to everyone who works on all the awesome Python data sciencelibraries like numpy, scipy, scikit-image,pillow, etc, etc that makes this kind of stuff so easy and fun inPython.
  • Thanks to Cookiecutterand theaudreyr/cookiecutter-pypackageproject templatefor making Python project packaging way more tolerable.

History

1.2.3 (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 3.7
  • New example of using this library in a Jupyter Notebook

1.2.2 (2018-04-02)

  • Added the face_detection CLI command
  • Removed dependencies on scipy to make installation easier
  • Cleaned up KNN example and fixed a bug with drawing fonts to label detected faces in the demo

1.2.1 (2018-02-01)

  • Fixed version numbering inside of module code.

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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 conversion
  • Updated webcam examples to avoid common mistakes and reduce support questions
  • Added a KNN classification example
  • Added an example of automatically blurring faces in images or videos
  • Updated 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 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 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

0.2.2 (2017-07-07)

  • Added –show-distance to cli
  • Fixed a bug where –tolerance was ignored in cli if testing a single image
  • Added benchmark.py to examples

0.2.0 (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 example
  • Improved CLI tests to actually test the CLI functionality
  • Updated facerec_on_raspberry_pi.py to capture in rgb (not bgr) format.

0.1.14 (2017-04-22)

  • Fixed a ValueError crash when using the CLI on Python 2.7

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)

Face Recognition Login Win 10 Software

  • Fix minimum scipy version required.

0.1.7 (2017-03-13)

  • First working release.

Release historyRelease notifications | RSS feed

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Face Recognition Login Ubuntu

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Macbook pro update mojave. 0.1.1

0.1.0

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