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Face detection using Random projections

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This study presents an innovative approach for face detection using random projections to reduce high-dimensional data into a manageable low dimension (100-1000 dimensions). By leveraging a projection matrix, pairwise distances are preserved with high probability while employing SVM classification with a polynomial kernel (degree 3) on tested datasets such as Essex Faces (360 images), Sheffield Faces (575 images), Georgia Tech Faces (750 images), and Caltech Faces (436 images). Future work includes exploring different probability distributions for projection matrix calculation and comparing this method against PCA.

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Face detection using Random projections

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Presentation Transcript

  1. Face detection using Random projections Sunil Khanal

  2. 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

  3. The theory Robustness (  ) of a concept class: Letberandomprojections of        . Given a threshold   , and target dimension k Projectingfrom a 50,000 dimensionspaceto 1000 dimension preserves pairwisedistancestowith          % probability

  4. 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)

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

  6. 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

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