README.md

# Face Recognition

Recognize and manipulate faces from Python or from the command line with
the world's simplest face recognition library.

Built using [dlib](http://dlib.net/)'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](http://vis-www.cs.umass.edu/lfw/) 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!

![](https://img.shields.io/pypi/v/face_recognition.svg)
![](https://travis-ci.org/ageitgey/face_recognition)

## Features

#### Find faces in pictures

Find all the faces that appear in a picture:

![](https://cloud.githubusercontent.com/assets/896692/23625227/42c65360-025d-11e7-94ea-b12f28cb34b4.png)

```python
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.

![](https://cloud.githubusercontent.com/assets/896692/23625282/7f2d79dc-025d-11e7-8728-d8924596f8fa.png)

```python
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 for really stupid stuff
like applying [digital make-up](https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py) (think 'Meitu'):

![](https://cloud.githubusercontent.com/assets/896692/23625283/80638760-025d-11e7-80a2-1d2779f7ccab.png)

#### Identify faces in pictures

Recognize who appears in each photo.

![](https://cloud.githubusercontent.com/assets/896692/23625229/45e049b6-025d-11e7-89cc-8a71cf89e713.png)

```python
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)
```

## Installation

Python 3 is fully supported. Python 2 should also work. Only macOS and
Linux are tested. I have no idea if this will work on Windows.

1. First, install the required machine learning models using `pip3` (or `pip2` for Python 2):

```bash
pip3 install git+https://github.com/ageitgey/face_recognition_models
```

2. Then you can install this module from pypi using `pip3` (or `pip2` for Python 2):

```bash
pip3 install face_recognition
```

NOTE: It's very likely that you will run into problems when pip tries to compile
the `dlib` dependency. If that happens, check out this guide to installing
dlib from source instead to fix the error:

[How to install dlib from source](https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf)

After manually installing `dlib`, try running `pip3 install face_recognition`
again.

## Usage

#### Command-Line Interface

When you install `face_recognition`, you get a simple command-line program
called `face_recognition` that you can use to 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:

![known](https://cloud.githubusercontent.com/assets/896692/23582466/8324810e-00df-11e7-82cf-41515eba704d.png)

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

![unknown](https://cloud.githubusercontent.com/assets/896692/23582465/81f422f8-00df-11e7-8b0d-75364f641f58.png)

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:

```bash
$ 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.

If you simply want to know the names of the people in each photograph but don't
care about file names, you could do this:

```bash
$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2

Barack Obama
unknown_person
```


#### Python Module

You can import the `face_recognition` module and then easily manipulate
faces with just a couple of lines of code. It's super easy!

API Docs: [https://face-recognition.readthedocs.io](https://face-recognition.readthedocs.io).

##### Automatically find all the faces in an image

```python
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!
```

See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py)
 to try it out.

##### Automatically locate the facial features of a person in an image

```python
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.
```

See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py)
 to try it out.

##### Recognize faces in images and identify who they are

```python
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!")
```

See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py)
 to try it out.


## Python Code Examples

All the examples are available [here](https://github.com/ageitgey/face_recognition/tree/master/examples).

* [Find faces in an photograph](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py)
* [Identify specific facial features in a photograph](https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py)
* [Apply (horribly ugly) digital make-up](https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py)
* [Find and recognize unknown faces in a photograph based on photographs of known people](https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py)

## How Face Recognition Works

If you want to learn how face location and recognition work instead of
depending on a black box library, [read my article](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78).

## 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.

## Thanks

* Many, many thanks to [Davis King](https://github.com/davisking) ([@nulhom](https://twitter.com/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](http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html).
* 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](https://github.com/audreyr/cookiecutter) and the
  [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template
  for making Python project packaging way more tolerable.