39 lines
1.4 KiB
Mathematica
39 lines
1.4 KiB
Mathematica
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function [ decision ] = face_check_wild( img, shape, global_params )
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%FACE_CHECK_WILD Summary of this function goes here
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% Detailed explanation goes here
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load '../models/pdm/pdm_68_multi_pie.mat';
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load '../models/tri_68.mat';
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load '../face_validation_svr/trained/face_check_general_68.mat';
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% first need to determine the view
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centres = cat(1, faceCheckers.centres);
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dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1);
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[~,view_id] = min(sum(dists.^2,2));
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% shape = faceCheckers(view_id).source;
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img_crop = Crop(img, shape, faceCheckers(view_id).triangulation,...
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faceCheckers(view_id).triX, faceCheckers(view_id).mask,...
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faceCheckers(view_id).alphas, faceCheckers(view_id).betas,...
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faceCheckers(view_id).nPix, faceCheckers(view_id).minX, ...
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faceCheckers(view_id).minY);
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img_crop = reshape(img_crop(logical(faceCheckers(view_id).mask)), 1, faceCheckers(view_id).nPix);
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img_crop(isnan(img_crop)) = 0;
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% normalisation
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img_crop = (img_crop - mean(img_crop));
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norms = std(img_crop);
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if(norms==0)
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norms = 1;
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end
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img_crop = img_crop / norms;
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% Projection onto principal components
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img_crop = img_crop - faceCheckers(view_id).mean_ex;
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img_crop = img_crop ./ faceCheckers(view_id).std_ex;
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img_crop = img_crop * faceCheckers(view_id).principal_components;
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decision = faceCheckers(view_id).b + faceCheckers(view_id).w' * img_crop';
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