Slide 1

Report
Face Recognition Using
Eigenfaces
Obama and Biden, McCain and Palin
Justin Li
What’s Face Recognition Good For?
Security Systems
Smart Artificial Systems
New Human-Machine
Interface Methods
A Survey of Methods
Eigenfaces
Facial Measurements Mapping
3D Morphable Model
Facial Feature Mapping
Brief History of Eigenfaces
Matthew Turk
Alex Pentland
Michael Kirby
Lawrence Sirovich
The Eigenface Approach (1)
• Name comes from the use of
eigenvectors to identify faces.
• Principal Component Analysis
– Takes the mean of the pictures
from a grayscale training set.
– Subtracts the calculated mean
from each picture.
– Forms a covariance matrix.
– Finds the eigenvectors for the
covariance vectors.
• PCA gives the resultant
eigenvectors/eigenfaces.
The Eigenface Approach (2)
• Weighs test images with the
eigenvectors to find correlation.
• A computationally fast method
for face recognition.
• Note that the covariance matrix
will be exceedingly large.
– A simplification is introduced.
– Instead, make a simplified matrix
with dimensions the number of
pictures in the training set.
– Scale the eigenvectors using the
training pictures.
• This allows for a computationally
feasible way to calculate the
eigenfaces.
Flaws and Limitations
• Lighting conditions
• Requires specific alignment
and normalization.
• Face orientation
Implementation and Improvements
• Standard implementation as given in the
paper by Pentland and Turk.
• Improvements possible with better
segmentation and cropping, perhaps
ignoring more portions of the hair.
• Combining method with results from other
facial metrics or from other methods
entirely.
Demonstration and
Questions?

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