BEYOND SIMPLE FEATURES: A LARGE-SCALE FEATURE SEARCH APPROACH TO UNCONSTRAINED FACE RECOGNITION Nicolas Pinto Massachusetts Institute of Technology David Cox The Rowland Institute at Harvard, Harvard University International Conference on Automatic Face and Gesture Recognition (FG), 2011.
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Nicolas Pinto Massachusetts Institute of Technology David Cox
Beyond Simple Features: A Large-Scale Feature Search Approach to Unconstrained Face Recognition. International Conference on Automatic Face and Gesture Recognition (FG), 2011. Nicolas Pinto Massachusetts Institute of Technology David Cox - PowerPoint PPT Presentation
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BEYOND SIMPLE FEATURES: A LARGE-SCALE FEATURE SEARCH
APPROACH TO UNCONSTRAINED FACE
RECOGNITION
Nicolas Pinto Massachusetts Institute of TechnologyDavid CoxThe Rowland Institute at Harvard, Harvard University
International Conference on Automatic Face and Gesture Recognition (FG), 2011.
capture aspects of the computational architecture of the brain and mimic its computational abilities
Introduction Large Scale Feature Search Framework
Generate models with different parameters then screening
Method - V1-like visual representation
“Null model” - only represent first-order description of the primary visual cortex
Detail Preprocessing: resize image to 150 pixels with aspect
ratio preserved using bicubic interpolation Input normalization: divide each pixel’s intensity value
by the norm of the pixels in the 3x3 neighboring region Gabor wavelet: 16 orientation, 6 spatial frequencies Output normalization: divide by the norm of the pixels
in the 3x3 neighboring region Thresholding and Clipping: output value not in (0,1) is
Draws biological inspiration from the competitive interactions observed in natural neuronal systems (e.g. contrast gain control mechanisms in cortical area V1, and elsewhere)