IMPORTANT: you will need openCV library in order to get it run and work.
Final Year Project in INTI International University Malaysia
Face Detection: Haar Cascades
Feature Extraction: Principle Component Analysis
Face Recognition: Eigenfaces
Steps
1.Grayscale Face Images
2.A “STOP” Exposure
3.Calculate Eigenfaces(Corresponding to largest Eigenvalue)
Dataset training
1.Automatically Crop Face Images
2.Grayscale and Resize Face images to 220 * 220 pixel
3.Feature Extraction and Calculate distance
4.Distance below 0.2 can be identified as same person
1.Steps of Face Recognition from image
2.Load Face Image
3.Grayscale, a “STOP” Exposure and Resize
4.Face Detection using Haar Cascades
5.Feature Extraction using PCA
6.Calculate Eigenvalues and Similarity
7.Retrieve results from Face Database
Fast Face Detection and Recognition within seconds
Secured based Email Notification
Detection and Recognition under difficult Light Conditions from 50 lux to 8000 lux
Low Cost Implementation and no additional Cost