facetrain.m

基于贝叶斯理论的指纹识别算法及学习套件, 使用贝叶斯概率论实现对指纹识别,特征码提取,特征对数获取的功能

% Author: Scott Sanner
% Email:  [e-mail]
% Course: CS223B, Winter
% Desc:   Main training implementation of the Rowley-Beluja-Kanade face detector
%
% For now, just a script... make it so that a picture filename can be passed

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%%%                                 Setup
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% Neural net constants
FACE_T =  0.9;
FACE_F = -0.9;

% Load the image oval mask
MASK = buildmask;
NI   = size(find(MASK),1);

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%%%                              Image Loading
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% Load the face images, normalize, and set training vectors
FACES = loadimages('./scaled/s', {'01' '02' '04' '07' '11' '99'}, ...
        {'c' 'l' 'r' 'n' 'g'}, 'PNG', 1);
FACES = augmentlr(FACES);
[NORM_FACES, SHADING] = normalize(FACES, MASK);
% Expand image set here
FACEV = buildimvector(NORM_FACES, MASK);
FACER = buildresvector(NORM_FACES, FACE_T);

% Load the non-face images, normalize, and set training vectors
NFACES = loadimages('./scaled/n', ...
	{'01' '02' '03' '04' '05' '06' '07' '08' '09' '10' '11' '12' '13' '14' ...
	 '15' '16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' ... 
         '29' '30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40'}, ...
       	{'x'}, 'PNG', 1);
NFACES = augmentlr(NFACES);
NFACES = augmentud(NFACES);
[NORM_NFACES, NSHADING] = normalize(NFACES, MASK);
% Expand image set here
NFACEV = buildimvector(NORM_NFACES, MASK);
NFACER = buildresvector(NORM_NFACES, FACE_F);

% Test images
AmyBW  = double(imread('./scaled/AmyBW.JPG'));
GradBW = double(imread('./scaled/GradBW.JPG'));
HeadsBW = double(imread('./scaled/HeadsBW.JPG'));

% Display images
% showimages(NORM_FACES,  5, 10, 1, 50, 1);
% showimages(NORM_NFACES, 5, 5, 1, 25, 2);
% pause;

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%%%                          Neural Net Training
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% Build a neural net and train it
NET = createnn(NI, 25, 1, FACE_F, FACE_T);
[NET,PERF, ERR] = trainnn(NET,[NFACEV FACEV], [NFACER FACER], .10, 500);
figure(1); plot(PERF(:,1),PERF(:,2),'b-',PERF(:,1),PERF(:,3),'r-');
figure(2); plot(ERR (:,1),ERR (:,2),'b-',ERR (:,1),ERR (:,3),'r-');

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%%%                          Performance Testing
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% Test performance - for now just test on original images; extend to
% an image pyramid and return upper left corner of all 27x18 face 
% bounding boxes
NUM_FACES  = size(FACES,2);
NUM_NFACES = size(NFACES,2);

t0 = clock; ferr = 0;
for i=1:NUM_FACES,
  TEST = classifynn(NET, FACES{i}, MASK,1,1);
  fprintf(1, '(Target, Test, Match): (%f,%f,%d)\n', FACE_T, TEST, TEST > 0);   
  ferr = ferr + (TEST < 0);
end

nferr = 0;
for i=1:NUM_NFACES,
  TEST = classifynn(NET, NFACES{i}, MASK,1,1);
  fprintf(1, '(Target, Test, Match): (%f,%f,%d)\n', FACE_F, TEST, TEST < 0);   
  nferr = nferr + (TEST > 0);
end

fprintf(1, '\n(Face Err, Nonface Err, Total Err): (%1.3f,%1.3f,%1.3f)\n', ...
	ferr./NUM_FACES, nferr./NUM_NFACES, (ferr + nferr)./(NUM_FACES + NUM_NFACES));
fprintf(1, 'Time to classify %d images: %5.3f\n', NUM_FACES+NUM_NFACES, etime(clock,t0));

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