In this project, you will create a classifier for a face recognition system.
This classifier must be able to recognize the faces in images in
its training set, recognize when a face is not in the dataset, and recognize when there is not a face in the image.
|Images are grayscale.|
|All images are the same size and have been processed to make sure that
there the face is in the center of the image and the picture was taken at
the same range.|
The feature set will be the
eigenfaces with different numbers of eigenvalues. Use at least one additional
paper for ideas. For extra
credit, try LDA or ICA methods for feature extraction as well.
Design two classifiers to separate the face features (one linear, one
nonlinear, use cross validation. Validate the data on the test set. Give a confusion matrix.
The training data set will be a subset of the Yale Face DatabaseB. Contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). For every subject in a particular pose, an image with ambient (background) illumination was also captured. (Ref: Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J., "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", IEEE Trans. Pattern Anal. Mach. Intelligence, 23:6, 643-660, 2001). Project Data Set
|Yale Face Database The Yale Face Database (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink. (images)|
Page last edited 03/03/2006