17-08-2012, 04:32 PM
Face Recognition: A Literature Review
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2D Face Recognition Approaches
Neural networks
Back propagation techniques
Better for detection and localisation than identification
Feature analysis
Localisation of features
Distance between features
Feature characteristics
Graph matching
Construct a graph around the face
Possible need for feature localisation
Can include other data (colour, texture)
Eigenface
Information Theory approach
Identify discriminating components
Fisherface
Uses ‘within-class’ information to maximise class separation
Face Recognition though Geometric Features
Uses vertical and horizontal integral projections of edge maps.
The nose is found by searching for peaks in the vertical projection.
22 Geometrical features used.
Recognition performed by nearest neighbour.
Only useful for small databases, or preliminary step.
The Eigenface Method
Use PCA to determine the most discriminating features between images of faces.
Create an image subspace (face space) which best discriminates between faces.
Like faces occupy near points in face space.
Compare two faces by projecting the images into faces pace and measuring the distance between them.
Applying the same principal to faces
A 256x256 pixel image of a face occupies a single point in 65,536-dimensional image space.
Images of faces occupy a small region of this large image space.
Similarly, different faces should occupy different areas of this smaller region.
We can identify a face by finding the nearest ‘known’ face in image space.
PCA – Principal Component Analysis
Principal component analysis is used to calculate the vectors which best represent this small region of image space.
These are the eigenvectors of the covariance matrix for the training set.
The eigenvectors are used to define the subspace of face images, known as face space.
In Practice
Align a set of face images (the training set)
Rotate, scale and translate such that the eyes are located at the same coordinates.
Compute the average face image
Compute the difference image for
each image in the training set
Compute the covariance matrix
of this set of difference images
Compute the eigenvectors of the covariance matrix