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Face Recognition Using Eigenfaces. Obama and Biden, McCain and Palin. Justin Li. What’s Face Recognition Good For?. Smart Artificial Systems. Security Systems. New Human-Machine Interface Methods. A Survey of Methods. Eigenfaces. Facial Measurements Mapping. 3D Morphable Model.
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Face Recognition Using Eigenfaces Obama and Biden, McCain and Palin Justin Li
What’s Face Recognition Good For? Smart Artificial Systems Security Systems New Human-Machine Interface Methods
A Survey of Methods Eigenfaces Facial Measurements Mapping 3D Morphable Model Facial Feature Mapping
Lawrence Sirovich Alex Pentland Brief History of Eigenfaces Matthew Turk 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 • Lighting conditions • Requires specific alignment and normalization.
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.