Face detection using Random projections
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Transcript of Face detection using Random projections
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 testedEssex Faces (Expression variations, 360 images)
Caltech Faces (Varying lighting condition and background, 436 images)
Sheffield Faces (Pose variation, 575 images)
Georgia Tech Faces (Similar lighting condition and background, 750 images)
Results
10 110 210 310 410 510 610 710 810 910200030354045505560657075
Sheffield (20 persons) Georgia Tech (50 persons)
10 90 170
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Caltech (26 persons)
10 90 170
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570
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890
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Essex (18 persons)
10 110 210 310 410 510 610 710 810 91020000
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- SVM classification with polynomial kernel (degree 3)- 90% training, 10% testing
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