https://github.com/geekquad/facial-recognition-with-pca

Face Recognition Implementation using PCA, eigenfaces, and SVM
https://github.com/geekquad/facial-recognition-with-pca

eigenfaces face-recognition gridsearchcv lfw-dataset pca sklearn svm-classifier

Last synced: 4 months ago
JSON representation

Face Recognition Implementation using PCA, eigenfaces, and SVM

• Host: GitHub
• URL: https://github.com/geekquad/facial-recognition-with-pca
• Owner: geekquad
• Created: 2020-07-14T09:59:19.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-07-15T21:11:38.000Z (almost 5 years ago)
• Last Synced: 2025-01-07T05:16:46.531Z (6 months ago)
• Topics: eigenfaces, face-recognition, gridsearchcv, lfw-dataset, pca, sklearn, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 482 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## PCA

PCA is a systemized way to transform input features into principle components, then those principle components are used as **new features.**

It is basiclly a mathematical technique used for dimensionality reduction.

### Working:

PCA finds a new coordinate system that is obtained from the old one by translation and rotation only. and it moves the center of the coordinate sysetm with the centre of the data.

##### PCA is resolved as:





The amount of information we loose is equal to the distance between a given point(as it existed in the sort od 2D space) and its new spot in the line. 

#### Documentation:

 https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html 

 


## Facial Recognition using PCA:

One of the simplest and most effective PCA approaches used

in face recognition systems is the so-called eigenface

approach. This approach transforms faces into a small set of

essential characteristics, eigenfaces, which are the main

components of the initial set of learning images (training set).

Recognition is done by projecting a new image in the

eigenface subspace, after which the person is classified by

comparing its position in eigenface space with the position of

known individuals. 

##### Dataset:  https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html 

### Evaluation:

**Classification Report:**

 

 

                    precision    recall  f1-score   support

    Ariel Sharon      0.60      0.69      0.64        13

    Colin Powell      0.78      0.88      0.83        60

    Donald Rumsfeld   0.73      0.70      0.72        27

    George W Bush     0.91      0.88      0.90       146

    Gerhard Schroeder 0.81      0.88      0.85        25

    Hugo Chavez       0.73      0.53      0.62        15

    Tony Blair        0.91      0.86      0.89        36

    accuracy                              0.84       322

    macro avg         0.78      0.78      0.78       322

    weighted avg      0.84      0.84      0.84       322

    

## Prediction:

**(a) The most significant faces:** 



**(b) Eigenfaces:**