Skip to content
This repository has been archived by the owner on Aug 23, 2024. It is now read-only.
/ EZfaces Public archive

Python package that implements Eigenfaces to build a face recognition database. It supports interaction with the webcam.

License

Notifications You must be signed in to change notification settings

leonmavr/EZfaces

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

96 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

EZfaces

Easily create your own face recognition system in Python using Eigenfaces


Description

Python package
License: GPL v3

A tool for face recognition in Python. it implements Turk and Pentland's paper. The notation follows my pdf notes here. Finally, it is based on the Olivetti faces dataset. Some of its features are:

  • Load Olivetti faces to initialise dataset.
  • Load new subjects from file.
  • Read new subjects directly from webcam.
  • Predict a novel face from file.
  • Predict novel face from webcam.
  • Export currently loaded dataset and load it later.
  • Built-in benchmarking (classification report) method.

Note: When you add a new subject, it is recommended to take several (5 or more) pictures of its face profile from slighly different small angles.

Installation

You can install the package as follows:

cd <project_root>
pip install .

Next, you can import the package as import ezfaces or its main class as from ezfaces.face_classifier import FaceClassifier.

The project has been tested in CI (see workflows) in Python 3.7 and 3.8 with the following dependencies installed, but newer versions will also work:

opencv-python 4.1.2.30
numpy 1.17.4
matplotlib 3.1.2
scipy 1.4.1
scikit-image 0.16.2
scikit-learn 0.22

Usage examples

1. Load new subject from folder

from ezfaces.face_classifier import FaceClassifier

fc = FaceClassifier()
lbl_new = fc.add_img_data('tests/images_yale')
print(fc)
print("New subject\'s label is %d" % lbl_new)

Output:

Loaded 410 samples in total.
348 for training and 61 for testing.
New subject's label is 40

2. Load new subject and predict from webcam

from ezfaces.face_classifier import FaceClassifier
import cv2


fc = FaceClassifier()
lbl_new = fc.add_img_data(from_webcam=True)
fc.train()
# take a snapshot from webcam
x_novel = fc.webcam2vec()
x_pred, lbl_pred = fc.classify(x_novel)
print("The ID of the newly added subject is %d. The prediction from "
        "the webcam is %d" %(lbl_new, lbl_pred))
cv2.imshow("Prediction", fc.vec2img(x_pred))
cv2.waitKey(3000)
cv2.destroyAllWindows()

demo

3. Export and import dataset

from ezfaces.face_classifier import FaceClassifier


fc = FaceClassifier()
data_file, lbl_file = fc.export('/tmp')

# add some data
lbl_new = fc.add_img_data('tests/images_yale')
print(fc)

# now let's say we made a mistake and don't like the new data
fc = FaceClassifier(data_pkl = data_file, target_pkl = lbl_file)
print(fc)

Output:

Wrote data and target as .pkl at:
/tmp
Loaded 410 samples in total.
348 for training and 61 for testing.
Loaded 400 samples in total.
340 for training and 60 for testing.

4. Show all loaded subjects

from ezfaces.face_classifier import FaceClassifier


fc = FaceClassifier()
# add some adata
lbl_new = fc.add_img_data('tests/images_yale')
fc.show_album()

It will show the subjects in 8*8 image grids as follows: album

About

Python package that implements Eigenfaces to build a face recognition database. It supports interaction with the webcam.

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages