facerelevance.m

It is for Face Recognition

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function faceRelevance()
%   z.li, 03-18-2004
%   find face relevance to a model with certain distance metric
% function dependency:
%   - faceDistance(metric, v1, v2)
% input:
%   prj - feature space as the d x nFace*nPose prj matrix 
%   model - [id f1 f2 f3 f4 ... fm] id's m faces 
%           identifying certain person
%   metric - 'eucl', 'ker1', 'ker2'...etc
%   idf -  [id f] face in question
% output:
%   relv - [0 1] value
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%function [relv]=faceRelevance(prj, metric, model, idf)
function [relv]=faceRelevance(prj, metric, model, idf)
 dbg = 'y';
 if dbg == 'y'
    prj = load('prjAttFacesSide05Top10Low15W16H20LumaMin60Max150.dat');
    model = [30 9 10 8 2 7];
    metric = 'eucl';
    idf = [20 6];
 end

 % const
 dbgPlot = 'y';
 nFace = 40; nPose = 10;
 alpha = 0.001; nd = 12;

 % compute distance vector to the model
 qId = idf(1); qFn = idf(2); 
 qVec  = prj(1:nd,(qId-1)*nPose+qFn);
 moId = model(1); moFn = model(2:end); m = length(moFn);
 for k=1:m   
   moVec = prj(1:nd,(moId-1)*nPose+moFn(k));
   if moVec == inf
     dv(k) = inf;
   else
     dv(k) = faceDistance(metric, qVec, moVec);
   end
 end
  
 [dmin kmin] = min(dv);
 relv = exp(-alpha*dmin);