kfdfacerecognition.m

It is for Face Recognition

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function kfdFaceRecognition()
%   z.li, 06-14-2004
%   fisher discriminant analysis with local kernel mapping based
%   recognition
% function dependency:
%   - n/a
% input:
%   fm1, fm2, fm3   - face models in d x n
%   xId, xPose      - an unknown person
%   ids             - known persons
%   poses           - known poses
%   R               - [R1 R2 R3] local radius for 3 models
%   options         - 'linear', 'kernel', 'distnc'
% output:
%   results         -[rId1 err1 nModel1]
%                   -[rId2 err2 nModel2] 
%                   -[rId3 err3 nModel3]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%function [results]=kfdFaceRecognition(fm1, fm2, fm3, xId, xPose, ids, poses, R, minFaceCnt, options)
function [results]=kfdFaceRecognition(fm1, fm2, fm3, xId, xPose, ids, poses, R, minFaceCnt, options)
 dbg = 'n';
 if dbg=='y'
    % 18x16 upper face
    fm1 = load('prjATTUpperFaceS04T08L08W18H16K16.dat');
    % 14x18 lower face
    fm2 = load('prjATTLowerFaceS01T-06L14W18H12K12.dat');
    % 21x28 full face
    fm3 = load('prjATTFullFaceModel4.dat');    
    
    xId = 22; xPose = 5;
    ids = 11:30; poses = [1 2 3 4 6 7];
    R = [400 600 800];    
    options = 'kernel';
 end

 % const
 nFace = 40; nPose = 10;  
 if options == 'linear'      %'quadratic' 'linear', 'mahalanobis'
    method = 'linear';  
 elseif options == 'kernel'
    method = 'quadratic';  
 else
    method = 'mahalanobis';  
 end      

 % var
 R1 = R(1); R2 = R(2); R3 = R(3);

 % get the unknown face projection x1, x2, x3,  check for  existance
 x1 = getAttFaceProj(fm1, xId, xPose); 
 if isnan(x1(1))==1
    fprintf('\n face %d:%d has no proj in upper model, exit', xId, xPose);
    results=-2*ones(3,3);
    return;
 end
 
 x2 = getAttFaceProj(fm2, xId, xPose); 
 if isnan(x2(1))==1
    fprintf('\n face %d:%d has no proj in lower model, exit', xId, xPose);
    results=-2*ones(3,3);
    return;
 end

 x3 = getAttFaceProj(fm3, xId, xPose); 
 if isnan(x3(1))==1
    fprintf('\n face %d:%d has no proj in full model, exit', xId, xPose);
    results=-2*ones(3,3);
    return;
 end

 % build recognition models from known faces 
 [X1, Y1, pCnt1]=buildLocalFaceRecogModel(fm1, x1, R1, ids, poses);
 [X2, Y2, pCnt2]=buildLocalFaceRecogModel(fm2, x2, R2, ids, poses);
 [X3, Y3, pCnt3]=buildLocalFaceRecogModel(fm3, x3, R3, ids, poses);
 
 % purge models
 [X1, Y1, nModel1]=purgeFaceRecogModel(ids, pCnt1, X1, Y1, minFaceCnt);
 [X2, Y2, nModel2]=purgeFaceRecogModel(ids, pCnt2, X2, Y2, minFaceCnt);
 [X3, Y3, nModel3]=purgeFaceRecogModel(ids, pCnt3, X3, Y3, minFaceCnt);

 % local model evaluation and recognition
 if nModel1 ~= -1 
    [rId1, err1]=mixedClassify(x1', X1', Y1', method);
    if isinf(err1) 
        rId1=-1;
    end
 else
     rId1 = -1; err1= inf;    % not recognized
 end

 if nModel2 ~= -1 
    [rId2, err2]=mixedClassify(x2', X2', Y2', method);
    if isinf(err2) 
        rId2=-1;
    end
 else
    rId2 = -1;  err2=inf;   % not recognized
 end

 if nModel3 ~= -1 
     [rId3, err3]=mixedClassify(x3', X3', Y3', method);
     if isinf(err3) 
        rId3=-1;
     end
 else
     rId3 = -1; err3=inf;    % not recognized
 end

 results=[rId1 err1 nModel1;rId2 err2 nModel2; rId3 err3 nModel3];
 if dbg=='y'   
   [rId1 err1 nModel1]
   [rId2 err2 nModel2] 
   [rId3 err3 nModel3]
 end