Obama and Biden, McCain and Palin Face Recognition Using Eigenfaces Justin Li

What’s Face Recognition Good For? Security Systems New Human-Machine Interface Methods Smart Artificial Systems

A Survey of Methods Facial Feature Mapping 3D Morphable Model Eigenfaces Facial Measurements Mapping

Brief History of Eigenfaces Matthew Turk Alex Pentland Lawrence Sirovich Michael Kirby

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 Face orientation Requires specific alignment and normalization. Lighting conditions

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?