matlab-renlianshibie.rar_face_face recognition_renlianshibie

% RunFaceRecog.m % Main function that reads in files, applies pre-processing, calls face % recognition algorithm, and then plots resulting accuracy. % % Assumptions: % - you have a set of pre-processed face images (cropped, normalized, ..) % - multiple photographs of each individual to be recognized are % available % - (maybe?) non-face training data is available? % % Alan Brooks and Li Gao % Northwestern University - ECE 432 Advanced Computer Vision % Mod history: % 2004: % 5-May initial version % 11-May added UMIST database capability % 12-May prettied the plots, lowered memory requirements % 13-May added subfigure, speed up % 14-May faster again (caching training data & svd) % 18-May began FisherFace technique (Li Gao) % 19-May tweaked FisherFace, more work (AB) % 20-May tried smaller pictures (AB) % 25-May more tries on FisherFace (AB) % 26-May added class to ALAN database, smaller UMIST, fix Fisher (AB) % 31-May added maxFishFase, figure saving, produced final plots (AB) % 1-Jun tried unpreproc ALAN database (AB) % ========================================================================= function RunFaceRecog() % ------ User Parameters ------ % database type dbType = 'UMIST'; % 'ALAN' or 'UMIST' supported so far % algorithm & feature extraction parameters frAlgorithm = 'eigen'; % 'eigen' 'fisher' ...? nEigFace = 21; %21; % pick # of principal components (PCA) maxFishEigFace = 41; %Inf; % pick max number of PCA components to use % in first part of Fisherface algorithm threshFace = 15; %10; %5; % thresholds threshClass = 8; %5; %3; % caching parameters train.forceReread = 0; % 0 = use cached inputs if found, 1 = don't train.forceNewSvd = 0; % 0 = use cached SVD, 1 = don't (PCA) train.forceNewScat = 0; % 0 = use cached scatters, 1 = don't (LDA) % plotting parameters plots.intermediateOn = 1; plots.finalOn = 1; plots.savePlotsOn = 1; f = filesep; switch dbType case 'UMIST' % data location & type basePath = ['C:\Documents and Settings\alanb\My Documents\NWU\' ... 'ECE 432\final project\face_databases\UMIST']; %basePath = ['D:\My Documents\NWU\ECE 432 Comp Vision\' ... % 'face_databases\UMIST']; %basePath = ['C:\Documents and Settings\lgao\My Documents\' ... % 'ECE432\Projects\Facial Recognition\']; %db = 'croppedConsolPng'; %db = 'croppedConsolPng46x56'; %db = 'croppedConsolPng23x28'; db = 'croppedConsolPng23x28_subset'; % 3 train, 1 recog image imType = 'png'; case 'ALAN' % data location & type if isunix basePath = ['/Users/alanb/Documents/Riting & Labs/' ... 'Northwestern/Year 2 Q3 (Spring)/' ... 'EE 432 Computer Vision/final project/' ... 'face_databases/MUstudentPhotos']; else basePath = ['C:\Documents and Settings\alanb\My Documents\' ... 'NWU\ECE 432\final project\face_databases\' ... 'MUstudentPhotos']; %basePath = ['D:\My Documents\NWU\ECE 432 Comp Vision\' ... % 'face_databases\millikin']; end %db = 'thumbsMoreThan2prepro40x60'; %db = 'thumbsMoreThan2prepro10x15'; %db = 'tmbwClassOval40x60'; db = 'tmbwClassOval40x60_2'; % changed which ones in recog %db = 'tmbwClassppNo60x80g'; % no pre-processing imType = 'png'; otherwise error(sprintf('%s database type not understood.',dbType)) end train.path = fullfile(basePath,[db f 'train']); recog.path = fullfile(basePath,[db f 'recog']); % ------ Pre-Processing ------ % read all training images di = dir(fullfile(train.path,['*.' imType])); if isempty(di) fprintf('Err: Couldn''t find any %s files in training path:\n\t%s\n',... imType,train.path) return end inputTrainFile = fullfile(train.path,'storedInputs.mat'); foundCache = exist(inputTrainFile,'file'); if foundCache & ~train.forceReread % loads stored input images trainNow = train; load(inputTrainFile) trainNow.I = train.I; % only update desired vars trainNow.classNameTrue = train.classNameTrue; trainNow.classTrue = train.classTrue; train = trainNow; clear trainNow else % read training images info = imfinfo(fullfile(train.path,di(1).name)); Nx = info.Height; Ny = info.Width; M = length(di); % number of training images train.I = uint8(zeros(Nx,Ny,M)); % init for speed numOfClass = 0; % initialize the number of classes classBoundary = 0; prevIndex = 1; for i = 1:M % for each training image % Read and store class [I,mp] = imread(fullfile(train.path,di(i).name),imType); train.I(:,:,i) = I; % image %train.I(:,:,i) = adapthisteq(I,'clipLimit',0.001); %train.I(:,:,i) = imadjust(I); %train.I(:,:,i) = histeq(I); train.classNameTrue{i} = di(i).name(1:end-4); % specific for my names train.classTrue(i) = i; % Group & calculate statistics for each class % looks for new classes by 1st 2 letters in filename if i>1 if ~strcmp(di(i-1).name(1:2),di(i).name(1:2)) % class bndry j = i; classBoundary = 1; end end if i==M % force class bndry for last image j = i+1; classBoundary = 1; end if classBoundary numOfClass = numOfClass + 1; trainClass.className{numOfClass} = ['class_' di(j-1).name(1:2)]; trainClass.num(numOfClass) = j-prevIndex; X = double(reshape(... train.I(:,:,prevIndex:(j-1)),[Nx*Ny j-prevIndex]))./256; trainClass.mean{numOfClass} = mean(X,2); trainClass.classStartIndex(numOfClass) = prevIndex; trainClass.classEndIndex(numOfClass) = j-1; prevIndex = j; clear X; classBoundary = 0; end end % save cached images for next run save(inputTrainFile,'train','Nx','Ny','M','mp','trainClass') end % read all images to be recognized di = dir(fullfile(recog.path,['*.' imType])); if isempty(di) fprintf('Err: Couldn''t find any %s files in recog path:\n\t%s\n',... imType,recog.path) return end info = imfinfo(fullfile(recog.path,di(1).name)); if (info.Height ~= Nx) | (info.Width ~= Ny) error('recog images must be same size as training images') end M2 = length(di); % number of recog images recog.I = uint8(zeros(Nx,Ny,M2)); % init for speed for i = 1:M2 [I,mp] = imread(fullfile(recog.path,di(i).name),imType); recog.I(:,:,i) = I; % image %recog.I(:,:,i) = adapthisteq(I,'clipLimit',0.001); %recog.I(:,:,i) = imadjust(I); %recog.I(:,:,i) = histeq(I); recog.classNameTrue{i} = di(i).name(1:end-4); % specific for my naming switch dbType case 'ALAN' recog.classTrue{i} = ... % matches by filename strmatch(recog.classNameTrue{i}(1:end-1),train.classNameTrue); case 'UMIST' recog.classTrue{i} = ... % matches by filename strmatch(recog.classNameTrue{i}(1:2),train.classNameTrue); end recog.classEst(i) = NaN; end % plot input images if plots.intermediateOn figure,montage(reshape(train.I,[Nx Ny 1 M]),mp),title('training images') if plots.savePlotsOn, saveas(gcf,'training_images','png'), end %could use >>unix('/sw/bin/montage -h') figure,montage(reshape(recog.I,[Nx Ny 1 M2]),mp),title('recog images') if plots.savePlotsOn, saveas(gcf,'recog_images','png'), end end % ------ Call Face Recog Alorithm ------ switch frAlgorithm case 'eigen' % -- Compute EigenFaces using "train