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Chapter 9 - Face Recognition

Face recognition by Eigenfaces method :

  • identify a face from a database of faces
  • determine whether an image contains a face present in the database
  • to decide whether an image represents a face or not

We will be using the Yale Faces Database.
This database contains 120 greyscale images, representing the faces of 15 people. There are 8 images per person.


The method developed by Turk and Pentland defines the eigenfaces as the main axes obtained by carrying out a principal component analysis (PCA) of the vectors associated with the reference faces.
We will use singular value decomposition (SVD).

example of reconstruction of an image

Chapter 8 - Split & Merge

the split and merge algorithm is used for image segmentation.
it works like this:

  • Split the image into equal size regions
  • Calculate homogeneity for each region
  • If the region is homogeneous, then merge it with neighbors
  • The process is repeated until all regions pass the homogeneity test

Chapter 7 - Searches Images By Content

We will compute a measure of similarity between two images from the normalized histogram. This measure of similarity will be used in order to find images that are the most similar to a given image.

20 best matches of image 'Liontigre1.png'

Chapter 6 - Harris Corner Detector

Chapter 5 - Edge Detection

The goal of this practial work is to experiment various edge detectors.

  • comparison between the first and second order detectors
  • study of the impact of smoothing
  • removing non maxima answers of the detectors
  • evaluation in term of robustness and localization

Chapter 4 - Frequency

Chapter 3 - 2D Sampling & Aliasing

Chapter 2 - Fourier Transform

Chapter 1 - Introduction & Image Enhancement

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