Face detection using Random projections

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Presentation transcript:

Face detection using Random projections Sunil Khanal

Random Projections Randomly project high dimensional data into low dimension Given , project it to using projection matrix : Can use to generate each element of the projection matrix Use the projection matrix to reduce data to 100-1000 dimensions Use the output for classification

The theory Robustness ( ) of a concept class: Let be random projections of . Given a threshold , and target dimension k Projecting from a 50,000 dimension space to 1000 dimension preserves pairwise distances to with % probability

Datasets tested Essex Faces (Expression variations, 360 images) Sheffield Faces (Pose variation, 575 images) Georgia Tech Faces (Similar lighting condition and background, 750 images) Caltech Faces (Varying lighting condition and background, 436 images)

Results - SVM classification with polynomial kernel (degree 3) 90% training, 10% testing Georgia Tech (50 persons) Sheffield (20 persons) Caltech (26 persons) Essex (18 persons)

Planned Work Interpreting the projection Explore different probability distributions for calculating the projection matrix Explore probabilistic kernel methods (esp. Gaussian product kernel) on the projected data RP seemingly works well for faces, but sensitive to background changes Comparison against PCA/feature based approaches