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Iris.html
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PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
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</style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#2">Load train Data</a></li><li><a href="#3">Size of first image in dataset</a></li><li><a href="#4">Load Test Data</a></li><li><a href="#5">Calculate the number of images in each category.</a></li><li><a href="#6">Define Network Architecture</a></li><li><a href="#7">Specify Training Options</a></li><li><a href="#8">Train Network Using Training Data</a></li><li><a href="#9">Classify validation</a></li><li><a href="#10">Compute Accuracy</a></li><li><a href="#11">move diary file</a></li><li><a href="#12"><b>Test one at a time</b></a></li><li><a href="#13">Find the square root of the false matches to determine the subplots</a></li><li><a href="#14">Recalbirating true and false</a></li><li><a href="#15">start plotting mismatches</a></li><li><a href="#16">start plotting sample of correct match</a></li></ul></div><pre class="codeinput"><span class="comment">% Biometric Systems</span>
<span class="comment">% CYS616</span>
<span class="comment">% CONVOLUTIONAL NEURAL NETWORK FOR IRIS RECOGNITION</span>
clc; <span class="comment">% Clear the command window.</span>
close <span class="string">all</span>; <span class="comment">% Close all figures (except those of imtool.)</span>
clear; <span class="comment">% Erase all existing variables. Or clearvars if you want.</span>
workspace; <span class="comment">% Make sure the workspace panel is showing.</span>
reset(gpuDevice); <span class="comment">% Reset GPU memory</span>
diary <span class="string">Progress.txt</span>
diary <span class="string">on</span>
</pre><h2 id="2">Load train Data</h2><pre class="codeinput">categories = {
<span class="string">'001'</span>,<span class="string">'002'</span>,<span class="string">'003'</span>,<span class="string">'004'</span>,<span class="string">'005'</span>,<span class="string">'006'</span>,<span class="string">'007'</span>,<span class="string">'008'</span>,<span class="string">'009'</span>,<span class="string">'010'</span>, <span class="keyword">...</span>
<span class="string">'011'</span>,<span class="string">'012'</span>,<span class="string">'013'</span>,<span class="string">'014'</span>,<span class="string">'015'</span>,<span class="string">'016'</span>,<span class="string">'017'</span>,<span class="string">'018'</span>,<span class="string">'019'</span>,<span class="string">'020'</span>, <span class="keyword">...</span>
<span class="string">'021'</span>,<span class="string">'022'</span>,<span class="string">'023'</span>,<span class="string">'024'</span>,<span class="string">'025'</span>,<span class="string">'026'</span>,<span class="string">'027'</span>,<span class="string">'028'</span>,<span class="string">'029'</span>,<span class="string">'030'</span>, <span class="keyword">...</span>
<span class="string">'031'</span>,<span class="string">'032'</span>,<span class="string">'033'</span>,<span class="string">'034'</span>,<span class="string">'035'</span>,<span class="string">'036'</span>,<span class="string">'037'</span>,<span class="string">'038'</span>,<span class="string">'039'</span>,<span class="string">'040'</span>, <span class="keyword">...</span>
<span class="string">'041'</span>,<span class="string">'042'</span>,<span class="string">'043'</span>,<span class="string">'044'</span>,<span class="string">'045'</span>,<span class="string">'046'</span>,<span class="string">'047'</span>,<span class="string">'048'</span>,<span class="string">'049'</span>,<span class="string">'050'</span>, <span class="keyword">...</span>
<span class="string">'051'</span>,<span class="string">'052'</span>,<span class="string">'053'</span>,<span class="string">'054'</span>,<span class="string">'055'</span>,<span class="string">'056'</span>,<span class="string">'057'</span>,<span class="string">'058'</span>,<span class="string">'059'</span>,<span class="string">'060'</span>, <span class="keyword">...</span>
<span class="string">'061'</span>,<span class="string">'062'</span>,<span class="string">'063'</span>,<span class="string">'064'</span>,<span class="string">'065'</span>,<span class="string">'066'</span>,<span class="string">'067'</span>,<span class="string">'068'</span>,<span class="string">'069'</span>,<span class="string">'070'</span>, <span class="keyword">...</span>
<span class="string">'071'</span>,<span class="string">'072'</span>,<span class="string">'073'</span>,<span class="string">'074'</span>,<span class="string">'075'</span>,<span class="string">'076'</span>,<span class="string">'077'</span>,<span class="string">'078'</span>,<span class="string">'079'</span>,<span class="string">'080'</span>, <span class="keyword">...</span>
<span class="string">'081'</span>,<span class="string">'082'</span>,<span class="string">'083'</span>,<span class="string">'084'</span>,<span class="string">'085'</span>,<span class="string">'086'</span>,<span class="string">'087'</span>,<span class="string">'088'</span>,<span class="string">'089'</span>,<span class="string">'090'</span>, <span class="keyword">...</span>
<span class="string">'091'</span>,<span class="string">'092'</span>,<span class="string">'093'</span>,<span class="string">'094'</span>,<span class="string">'095'</span>,<span class="string">'096'</span>,<span class="string">'097'</span>,<span class="string">'098'</span>,<span class="string">'099'</span>,<span class="string">'100'</span>, <span class="keyword">...</span>
<span class="string">'101'</span>,<span class="string">'102'</span>,<span class="string">'103'</span>,<span class="string">'104'</span>,<span class="string">'105'</span>,<span class="string">'106'</span>,<span class="string">'107'</span>,<span class="string">'108'</span>,<span class="string">'109'</span>,<span class="string">'110'</span>, <span class="keyword">...</span>
<span class="string">'111'</span>,<span class="string">'112'</span>,<span class="string">'113'</span>,<span class="string">'114'</span>,<span class="string">'115'</span>,<span class="string">'116'</span>,<span class="string">'117'</span>,<span class="string">'118'</span>,<span class="string">'119'</span>,<span class="string">'120'</span>, <span class="keyword">...</span>
<span class="string">'121'</span>,<span class="string">'122'</span>,<span class="string">'123'</span>,<span class="string">'124'</span>,<span class="string">'125'</span>,<span class="string">'126'</span>,<span class="string">'127'</span>,<span class="string">'128'</span>,<span class="string">'129'</span>,<span class="string">'130'</span>, <span class="keyword">...</span>
<span class="string">'131'</span>,<span class="string">'132'</span>,<span class="string">'133'</span>,<span class="string">'134'</span>,<span class="string">'135'</span>,<span class="string">'136'</span>,<span class="string">'137'</span>,<span class="string">'138'</span>,<span class="string">'139'</span>,<span class="string">'140'</span>, <span class="keyword">...</span>
<span class="string">'141'</span>,<span class="string">'142'</span>,<span class="string">'143'</span>,<span class="string">'144'</span>,<span class="string">'145'</span>,<span class="string">'146'</span>,<span class="string">'147'</span>,<span class="string">'148'</span>,<span class="string">'149'</span>,<span class="string">'150'</span>, <span class="keyword">...</span>
<span class="string">'151'</span>,<span class="string">'152'</span>,<span class="string">'153'</span>,<span class="string">'154'</span>,<span class="string">'155'</span>,<span class="string">'156'</span>,<span class="string">'157'</span>,<span class="string">'158'</span>,<span class="string">'159'</span>,<span class="string">'160'</span>, <span class="keyword">...</span>
<span class="string">'161'</span>,<span class="string">'162'</span>,<span class="string">'163'</span>,<span class="string">'164'</span>,<span class="string">'165'</span>,<span class="string">'166'</span>,<span class="string">'167'</span>,<span class="string">'168'</span>,<span class="string">'169'</span>,<span class="string">'170'</span>, <span class="keyword">...</span>
<span class="string">'171'</span>,<span class="string">'172'</span>,<span class="string">'173'</span>,<span class="string">'174'</span>,<span class="string">'175'</span>,<span class="string">'176'</span>,<span class="string">'177'</span>,<span class="string">'178'</span>,<span class="string">'179'</span>,<span class="string">'180'</span>, <span class="keyword">...</span>
<span class="string">'181'</span>,<span class="string">'182'</span>,<span class="string">'183'</span>,<span class="string">'184'</span>,<span class="string">'185'</span>,<span class="string">'186'</span>,<span class="string">'187'</span>,<span class="string">'188'</span>,<span class="string">'189'</span>,<span class="string">'190'</span>, <span class="keyword">...</span>
<span class="string">'191'</span>,<span class="string">'192'</span>,<span class="string">'193'</span>,<span class="string">'194'</span>,<span class="string">'195'</span>,<span class="string">'196'</span>,<span class="string">'197'</span>,<span class="string">'198'</span>,<span class="string">'199'</span>,<span class="string">'200'</span>, <span class="keyword">...</span>
<span class="string">'201'</span>,<span class="string">'202'</span>,<span class="string">'203'</span>,<span class="string">'204'</span>,<span class="string">'205'</span>,<span class="string">'206'</span>,<span class="string">'207'</span>,<span class="string">'208'</span>,<span class="string">'209'</span>,<span class="string">'210'</span>, <span class="keyword">...</span>
<span class="string">'211'</span>,<span class="string">'212'</span>,<span class="string">'213'</span>,<span class="string">'214'</span>,<span class="string">'215'</span>,<span class="string">'216'</span>,<span class="string">'217'</span>,<span class="string">'218'</span>,<span class="string">'219'</span>,<span class="string">'220'</span>, <span class="keyword">...</span>
<span class="string">'221'</span>,<span class="string">'222'</span>,<span class="string">'223'</span>};
imdsTrain = imageDatastore(fullfile(pwd,<span class="string">'Dataset/TrainData'</span>, categories),<span class="string">'IncludeSubfolders'</span>,true,<span class="string">'FileExtensions'</span>,<span class="string">'.bmp'</span>,<span class="string">'LabelSource'</span>,<span class="string">'foldernames'</span>);
num_train = size(imdsTrain.Files,1);
</pre><h2 id="3">Size of first image in dataset</h2><pre class="codeinput"> img = readimage(imdsTrain,1);
[x , y , z] = size(img);
</pre><h2 id="4">Load Test Data</h2><pre class="codeinput"> imdsValidation = imageDatastore(fullfile(pwd,<span class="string">'Dataset/TestData'</span>, categories),<span class="string">'IncludeSubfolders'</span>,true,<span class="string">'FileExtensions'</span>,<span class="string">'.bmp'</span>,<span class="string">'LabelSource'</span>,<span class="string">'foldernames'</span>);
num_test = size(imdsValidation.Files,1);
</pre><h2 id="5">Calculate the number of images in each category.</h2><pre class="codeinput"> labelCount = countEachLabel(imdsTrain);
</pre><h2 id="6">Define Network Architecture</h2><pre class="codeinput"> layers = [
imageInputLayer([x y z]);
convolution2dLayer(3,8,<span class="string">'Padding'</span>,<span class="string">'same'</span>)
batchNormalizationLayer
reluLayer();
maxPooling2dLayer(5,<span class="string">'Stride'</span>,2)
convolution2dLayer(3,16,<span class="string">'Padding'</span>,<span class="string">'same'</span>)
batchNormalizationLayer
reluLayer();
averagePooling2dLayer(5,<span class="string">'Stride'</span>,2)
convolution2dLayer(3,16,<span class="string">'Padding'</span>,<span class="string">'same'</span>)
batchNormalizationLayer
reluLayer();
maxPooling2dLayer(5,<span class="string">'Stride'</span>,2)
convolution2dLayer(3,32,<span class="string">'Padding'</span>,<span class="string">'same'</span>)
batchNormalizationLayer
reluLayer();
averagePooling2dLayer(5,<span class="string">'Stride'</span>,2)
convolution2dLayer(3,32,<span class="string">'Padding'</span>,<span class="string">'same'</span>)
batchNormalizationLayer
reluLayer();
fullyConnectedLayer(size(categories,2),<span class="string">'BiasLearnRateFactor'</span>,2);
softmaxLayer
classificationLayer];
</pre><h2 id="7">Specify Training Options</h2><pre class="codeinput"> options = trainingOptions(<span class="string">'sgdm'</span>, <span class="keyword">...</span>
<span class="string">'InitialLearnRate'</span>, 0.001, <span class="keyword">...</span>
<span class="string">'ValidationData'</span>,imdsValidation, <span class="keyword">...</span>
<span class="string">'ValidationFrequency'</span>,100, <span class="keyword">...</span>
<span class="string">'Shuffle'</span>,<span class="string">'every-epoch'</span>, <span class="keyword">...</span>
<span class="string">'MaxEpochs'</span>, 10, <span class="keyword">...</span>
<span class="string">'MiniBatchSize'</span>, 8, <span class="keyword">...</span>
<span class="string">'LearnRateSchedule'</span>,<span class="string">'piecewise'</span>, <span class="keyword">...</span>
<span class="string">'LearnRateDropFactor'</span>,0.5, <span class="keyword">...</span>
<span class="string">'LearnRateDropPeriod'</span>,50, <span class="keyword">...</span>
<span class="string">'ExecutionEnvironment'</span>,<span class="string">'gpu'</span>, <span class="keyword">...</span>
<span class="string">'Verbose'</span>, true, <span class="string">'VerboseFrequency'</span>, 10);
</pre><h2 id="8">Train Network Using Training Data</h2><pre class="codeinput"> [net_Wael, info] = trainNetwork(imdsTrain,layers,options);
save(<span class="string">'TrainedNetwork.mat'</span>,<span class="string">'net_Wael'</span>)
movefile(<span class="string">'TrainedNetwork.mat'</span>,<span class="string">'results'</span>)
</pre><pre class="codeoutput">Initializing input data normalization.
|======================================================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Validation | Mini-batch | Validation | Base Learning |
| | | (hh:mm:ss) | Accuracy | Accuracy | Loss | Loss | Rate |
|======================================================================================================================|
| 1 | 1 | 00:00:03 | 0.00% | 0.30% | 5.8989 | 5.8493 | 0.0010 |
| 1 | 10 | 00:00:03 | 0.00% | | 6.3078 | | 0.0010 |
| 1 | 20 | 00:00:04 | 0.00% | | 5.7647 | | 0.0010 |
| 1 | 30 | 00:00:04 | 12.50% | | 4.6819 | | 0.0010 |
| 1 | 40 | 00:00:05 | 0.00% | | 6.0511 | | 0.0010 |
| 1 | 50 | 00:00:05 | 12.50% | | 5.2941 | | 0.0010 |
| 1 | 60 | 00:00:06 | 25.00% | | 4.8073 | | 0.0010 |
| 1 | 70 | 00:00:06 | 0.00% | | 5.5946 | | 0.0010 |
| 1 | 80 | 00:00:07 | 0.00% | | 4.9676 | | 0.0010 |
| 1 | 90 | 00:00:08 | 12.50% | | 4.1375 | | 0.0010 |
| 1 | 100 | 00:00:11 | 25.00% | 20.48% | 4.5945 | 4.1891 | 0.0010 |
| 1 | 110 | 00:00:11 | 0.00% | | 5.6557 | | 0.0010 |
| 1 | 120 | 00:00:12 | 25.00% | | 4.4698 | | 0.0010 |
| 1 | 130 | 00:00:12 | 25.00% | | 3.4383 | | 0.0010 |
| 1 | 140 | 00:00:13 | 37.50% | | 4.0359 | | 0.0010 |
| 1 | 150 | 00:00:13 | 25.00% | | 3.3781 | | 0.0010 |
| 1 | 160 | 00:00:14 | 12.50% | | 3.2702 | | 0.0010 |
| 1 | 170 | 00:00:14 | 37.50% | | 2.7774 | | 0.0010 |
| 1 | 180 | 00:00:15 | 25.00% | | 3.2226 | | 0.0010 |
| 1 | 190 | 00:00:16 | 37.50% | | 2.7147 | | 0.0010 |
| 2 | 200 | 00:00:18 | 37.50% | 48.13% | 2.5095 | 2.4462 | 0.0010 |
| 2 | 210 | 00:00:19 | 75.00% | | 1.3925 | | 0.0010 |
| 2 | 220 | 00:00:19 | 50.00% | | 2.9466 | | 0.0010 |
| 2 | 230 | 00:00:20 | 50.00% | | 2.5476 | | 0.0010 |
| 2 | 240 | 00:00:20 | 75.00% | | 1.8553 | | 0.0010 |
| 2 | 250 | 00:00:21 | 50.00% | | 2.1814 | | 0.0010 |
| 2 | 260 | 00:00:22 | 75.00% | | 1.3891 | | 0.0010 |
| 2 | 270 | 00:00:22 | 50.00% | | 2.0654 | | 0.0010 |
| 2 | 280 | 00:00:23 | 87.50% | | 0.8888 | | 0.0010 |
| 2 | 290 | 00:00:23 | 75.00% | | 1.2809 | | 0.0010 |
| 2 | 300 | 00:00:26 | 87.50% | 70.70% | 1.7367 | 1.4797 | 0.0010 |
| 2 | 310 | 00:00:26 | 87.50% | | 0.7486 | | 0.0010 |
| 2 | 320 | 00:00:27 | 75.00% | | 1.1256 | | 0.0010 |
| 2 | 330 | 00:00:28 | 87.50% | | 0.9025 | | 0.0010 |
| 2 | 340 | 00:00:28 | 100.00% | | 0.7769 | | 0.0010 |
| 2 | 350 | 00:00:29 | 100.00% | | 0.5883 | | 0.0010 |
| 2 | 360 | 00:00:29 | 87.50% | | 0.6106 | | 0.0010 |
| 2 | 370 | 00:00:30 | 75.00% | | 0.6648 | | 0.0010 |
| 2 | 380 | 00:00:30 | 100.00% | | 0.4217 | | 0.0010 |
| 2 | 390 | 00:00:31 | 100.00% | | 0.5894 | | 0.0010 |
| 3 | 400 | 00:00:33 | 100.00% | 86.10% | 0.4794 | 0.8252 | 0.0010 |
| 3 | 410 | 00:00:34 | 100.00% | | 0.1444 | | 0.0010 |
| 3 | 420 | 00:00:35 | 87.50% | | 0.4606 | | 0.0010 |
| 3 | 430 | 00:00:35 | 75.00% | | 0.5521 | | 0.0010 |
| 3 | 440 | 00:00:36 | 100.00% | | 0.1855 | | 0.0010 |
| 3 | 450 | 00:00:36 | 87.50% | | 0.3960 | | 0.0010 |
| 3 | 460 | 00:00:37 | 87.50% | | 0.4560 | | 0.0010 |
| 3 | 470 | 00:00:37 | 87.50% | | 0.5018 | | 0.0010 |
| 3 | 480 | 00:00:38 | 100.00% | | 0.2638 | | 0.0010 |
| 3 | 490 | 00:00:38 | 87.50% | | 0.3632 | | 0.0010 |
| 3 | 500 | 00:00:41 | 100.00% | 91.33% | 0.1457 | 0.5355 | 0.0010 |
| 3 | 510 | 00:00:42 | 87.50% | | 0.3922 | | 0.0010 |
| 3 | 520 | 00:00:42 | 100.00% | | 0.1587 | | 0.0010 |
| 3 | 530 | 00:00:43 | 100.00% | | 0.1844 | | 0.0010 |
| 3 | 540 | 00:00:43 | 100.00% | | 0.1165 | | 0.0010 |
| 3 | 550 | 00:00:44 | 100.00% | | 0.6054 | | 0.0010 |
| 3 | 560 | 00:00:45 | 100.00% | | 0.0861 | | 0.0010 |
| 3 | 570 | 00:00:45 | 100.00% | | 0.0987 | | 0.0010 |
| 3 | 580 | 00:00:46 | 100.00% | | 0.2619 | | 0.0010 |
| 4 | 590 | 00:00:46 | 100.00% | | 0.0685 | | 0.0010 |
| 4 | 600 | 00:00:49 | 100.00% | 94.47% | 0.0628 | 0.4107 | 0.0010 |
| 4 | 610 | 00:00:50 | 100.00% | | 0.0989 | | 0.0010 |
| 4 | 620 | 00:00:50 | 100.00% | | 0.0897 | | 0.0010 |
| 4 | 630 | 00:00:51 | 100.00% | | 0.1051 | | 0.0010 |
| 4 | 640 | 00:00:51 | 100.00% | | 0.0589 | | 0.0010 |
| 4 | 650 | 00:00:52 | 100.00% | | 0.1304 | | 0.0010 |
| 4 | 660 | 00:00:52 | 100.00% | | 0.1253 | | 0.0010 |
| 4 | 670 | 00:00:53 | 100.00% | | 0.0776 | | 0.0010 |
| 4 | 680 | 00:00:53 | 100.00% | | 0.0596 | | 0.0010 |
| 4 | 690 | 00:00:54 | 100.00% | | 0.0640 | | 0.0010 |
| 4 | 700 | 00:00:57 | 100.00% | 95.81% | 0.0766 | 0.3357 | 0.0010 |
| 4 | 710 | 00:00:57 | 100.00% | | 0.0595 | | 0.0010 |
| 4 | 720 | 00:00:58 | 100.00% | | 0.0207 | | 0.0010 |
| 4 | 730 | 00:00:58 | 100.00% | | 0.0253 | | 0.0010 |
| 4 | 740 | 00:00:59 | 100.00% | | 0.0761 | | 0.0010 |
| 4 | 750 | 00:00:59 | 100.00% | | 0.1142 | | 0.0010 |
| 4 | 760 | 00:01:00 | 100.00% | | 0.0487 | | 0.0010 |
| 4 | 770 | 00:01:00 | 100.00% | | 0.0661 | | 0.0010 |
| 4 | 780 | 00:01:01 | 100.00% | | 0.0962 | | 0.0010 |
| 5 | 790 | 00:01:01 | 100.00% | | 0.0182 | | 0.0010 |
| 5 | 800 | 00:01:04 | 100.00% | 95.22% | 0.0296 | 0.3024 | 0.0010 |
| 5 | 810 | 00:01:05 | 100.00% | | 0.0279 | | 0.0010 |
| 5 | 820 | 00:01:05 | 100.00% | | 0.0272 | | 0.0010 |
| 5 | 830 | 00:01:06 | 100.00% | | 0.0298 | | 0.0010 |
| 5 | 840 | 00:01:06 | 100.00% | | 0.0548 | | 0.0010 |
| 5 | 850 | 00:01:07 | 100.00% | | 0.0499 | | 0.0010 |
| 5 | 860 | 00:01:07 | 100.00% | | 0.0150 | | 0.0010 |
| 5 | 870 | 00:01:08 | 100.00% | | 0.0194 | | 0.0010 |
| 5 | 880 | 00:01:09 | 100.00% | | 0.0747 | | 0.0010 |
| 5 | 890 | 00:01:09 | 100.00% | | 0.0809 | | 0.0010 |
| 5 | 900 | 00:01:12 | 100.00% | 96.71% | 0.0372 | 0.2745 | 0.0010 |
| 5 | 910 | 00:01:12 | 100.00% | | 0.0237 | | 0.0010 |
| 5 | 920 | 00:01:13 | 100.00% | | 0.0249 | | 0.0010 |
| 5 | 930 | 00:01:14 | 100.00% | | 0.0268 | | 0.0010 |
| 5 | 940 | 00:01:14 | 100.00% | | 0.0201 | | 0.0010 |
| 5 | 950 | 00:01:15 | 100.00% | | 0.0362 | | 0.0010 |
| 5 | 960 | 00:01:15 | 100.00% | | 0.0328 | | 0.0010 |
| 5 | 970 | 00:01:16 | 100.00% | | 0.0349 | | 0.0010 |
| 6 | 980 | 00:01:16 | 100.00% | | 0.0244 | | 0.0010 |
| 6 | 990 | 00:01:17 | 100.00% | | 0.0154 | | 0.0010 |
| 6 | 1000 | 00:01:20 | 100.00% | 96.56% | 0.0196 | 0.2753 | 0.0010 |
| 6 | 1010 | 00:01:20 | 100.00% | | 0.0427 | | 0.0010 |
| 6 | 1020 | 00:01:21 | 100.00% | | 0.0230 | | 0.0010 |
| 6 | 1030 | 00:01:21 | 100.00% | | 0.0460 | | 0.0010 |
| 6 | 1040 | 00:01:22 | 100.00% | | 0.0298 | | 0.0010 |
| 6 | 1050 | 00:01:22 | 100.00% | | 0.0227 | | 0.0010 |
| 6 | 1060 | 00:01:23 | 100.00% | | 0.0442 | | 0.0010 |
| 6 | 1070 | 00:01:23 | 100.00% | | 0.0120 | | 0.0010 |
| 6 | 1080 | 00:01:24 | 100.00% | | 0.1166 | | 0.0010 |
| 6 | 1090 | 00:01:25 | 100.00% | | 0.0333 | | 0.0010 |
| 6 | 1100 | 00:01:27 | 100.00% | 96.11% | 0.0192 | 0.2617 | 0.0010 |
| 6 | 1110 | 00:01:28 | 100.00% | | 0.0398 | | 0.0010 |
| 6 | 1120 | 00:01:28 | 100.00% | | 0.0294 | | 0.0010 |
| 6 | 1130 | 00:01:29 | 100.00% | | 0.0256 | | 0.0010 |
| 6 | 1140 | 00:01:29 | 100.00% | | 0.0682 | | 0.0010 |
| 6 | 1150 | 00:01:30 | 100.00% | | 0.0215 | | 0.0010 |
| 6 | 1160 | 00:01:31 | 100.00% | | 0.0304 | | 0.0010 |
| 6 | 1170 | 00:01:31 | 100.00% | | 0.0210 | | 0.0010 |
| 7 | 1180 | 00:01:32 | 100.00% | | 0.0117 | | 0.0010 |
| 7 | 1190 | 00:01:32 | 100.00% | | 0.0163 | | 0.0010 |
| 7 | 1200 | 00:01:35 | 100.00% | 95.96% | 0.0204 | 0.2605 | 0.0010 |
| 7 | 1210 | 00:01:36 | 100.00% | | 0.0172 | | 0.0010 |
| 7 | 1220 | 00:01:36 | 100.00% | | 0.0626 | | 0.0010 |
| 7 | 1230 | 00:01:37 | 100.00% | | 0.0245 | | 0.0010 |
| 7 | 1240 | 00:01:37 | 100.00% | | 0.0150 | | 0.0010 |
| 7 | 1250 | 00:01:38 | 100.00% | | 0.0216 | | 0.0010 |
| 7 | 1260 | 00:01:38 | 100.00% | | 0.0168 | | 0.0010 |
| 7 | 1270 | 00:01:39 | 100.00% | | 0.0243 | | 0.0010 |
| 7 | 1280 | 00:01:39 | 100.00% | | 0.0195 | | 0.0010 |
| 7 | 1290 | 00:01:40 | 100.00% | | 0.0259 | | 0.0010 |
| 7 | 1300 | 00:01:43 | 100.00% | 96.56% | 0.0450 | 0.2477 | 0.0010 |
| 7 | 1310 | 00:01:43 | 100.00% | | 0.0311 | | 0.0010 |
| 7 | 1320 | 00:01:44 | 100.00% | | 0.0367 | | 0.0010 |
| 7 | 1330 | 00:01:44 | 100.00% | | 0.0388 | | 0.0010 |
| 7 | 1340 | 00:01:45 | 100.00% | | 0.0157 | | 0.0010 |
| 7 | 1350 | 00:01:45 | 100.00% | | 0.0188 | | 0.0010 |
| 7 | 1360 | 00:01:46 | 100.00% | | 0.0161 | | 0.0010 |
| 8 | 1370 | 00:01:47 | 100.00% | | 0.0161 | | 0.0010 |
| 8 | 1380 | 00:01:47 | 100.00% | | 0.0246 | | 0.0010 |
| 8 | 1390 | 00:01:48 | 100.00% | | 0.0162 | | 0.0010 |
| 8 | 1400 | 00:01:50 | 100.00% | 96.86% | 0.0243 | 0.2382 | 0.0010 |
| 8 | 1410 | 00:01:51 | 100.00% | | 0.0179 | | 0.0010 |
| 8 | 1420 | 00:01:52 | 100.00% | | 0.0203 | | 0.0010 |
| 8 | 1430 | 00:01:52 | 100.00% | | 0.0103 | | 0.0010 |
| 8 | 1440 | 00:01:53 | 100.00% | | 0.0100 | | 0.0010 |
| 8 | 1450 | 00:01:53 | 100.00% | | 0.0158 | | 0.0010 |
| 8 | 1460 | 00:01:54 | 100.00% | | 0.0301 | | 0.0010 |
| 8 | 1470 | 00:01:54 | 100.00% | | 0.0258 | | 0.0010 |
| 8 | 1480 | 00:01:55 | 100.00% | | 0.0369 | | 0.0010 |
| 8 | 1490 | 00:01:55 | 100.00% | | 0.0241 | | 0.0010 |
| 8 | 1500 | 00:01:58 | 100.00% | 95.96% | 0.0207 | 0.2491 | 0.0010 |
| 8 | 1510 | 00:01:59 | 100.00% | | 0.0134 | | 0.0010 |
| 8 | 1520 | 00:01:59 | 100.00% | | 0.0139 | | 0.0010 |
| 8 | 1530 | 00:02:00 | 100.00% | | 0.0120 | | 0.0010 |
| 8 | 1540 | 00:02:00 | 100.00% | | 0.0206 | | 0.0010 |
| 8 | 1550 | 00:02:01 | 100.00% | | 0.0297 | | 0.0010 |
| 8 | 1560 | 00:02:01 | 100.00% | | 0.0320 | | 0.0010 |
| 9 | 1570 | 00:02:02 | 100.00% | | 0.0101 | | 0.0010 |
| 9 | 1580 | 00:02:02 | 100.00% | | 0.0545 | | 0.0010 |
| 9 | 1590 | 00:02:03 | 100.00% | | 0.0088 | | 0.0010 |
| 9 | 1600 | 00:02:06 | 100.00% | 96.41% | 0.0123 | 0.2439 | 0.0010 |
| 9 | 1610 | 00:02:06 | 100.00% | | 0.0150 | | 0.0010 |
| 9 | 1620 | 00:02:07 | 100.00% | | 0.0313 | | 0.0010 |
| 9 | 1630 | 00:02:07 | 100.00% | | 0.0161 | | 0.0010 |
| 9 | 1640 | 00:02:08 | 100.00% | | 0.0135 | | 0.0010 |
| 9 | 1650 | 00:02:09 | 100.00% | | 0.0152 | | 0.0010 |
| 9 | 1660 | 00:02:09 | 100.00% | | 0.0113 | | 0.0010 |
| 9 | 1670 | 00:02:10 | 100.00% | | 0.0139 | | 0.0010 |
| 9 | 1680 | 00:02:10 | 100.00% | | 0.0090 | | 0.0010 |
| 9 | 1690 | 00:02:11 | 100.00% | | 0.0134 | | 0.0010 |
| 9 | 1700 | 00:02:14 | 100.00% | 95.96% | 0.0139 | 0.2346 | 0.0010 |
| 9 | 1710 | 00:02:14 | 100.00% | | 0.0166 | | 0.0010 |
| 9 | 1720 | 00:02:15 | 100.00% | | 0.0131 | | 0.0010 |
| 9 | 1730 | 00:02:15 | 100.00% | | 0.0090 | | 0.0010 |
| 9 | 1740 | 00:02:16 | 100.00% | | 0.0107 | | 0.0010 |
| 9 | 1750 | 00:02:17 | 100.00% | | 0.0107 | | 0.0010 |
| 10 | 1760 | 00:02:17 | 100.00% | | 0.0093 | | 0.0010 |
| 10 | 1770 | 00:02:18 | 100.00% | | 0.0084 | | 0.0010 |
| 10 | 1780 | 00:02:18 | 100.00% | | 0.0078 | | 0.0010 |
| 10 | 1790 | 00:02:19 | 100.00% | | 0.0118 | | 0.0010 |
| 10 | 1800 | 00:02:22 | 100.00% | 96.56% | 0.0142 | 0.2341 | 0.0010 |
| 10 | 1810 | 00:02:22 | 100.00% | | 0.0159 | | 0.0010 |
| 10 | 1820 | 00:02:23 | 100.00% | | 0.0354 | | 0.0010 |
| 10 | 1830 | 00:02:23 | 100.00% | | 0.0094 | | 0.0010 |
| 10 | 1840 | 00:02:24 | 100.00% | | 0.0171 | | 0.0010 |
| 10 | 1850 | 00:02:24 | 100.00% | | 0.0161 | | 0.0010 |
| 10 | 1860 | 00:02:25 | 100.00% | | 0.0131 | | 0.0010 |
| 10 | 1870 | 00:02:25 | 100.00% | | 0.0350 | | 0.0010 |
| 10 | 1880 | 00:02:26 | 100.00% | | 0.0247 | | 0.0010 |
| 10 | 1890 | 00:02:27 | 100.00% | | 0.0101 | | 0.0010 |
| 10 | 1900 | 00:02:29 | 100.00% | 96.71% | 0.0116 | 0.2256 | 0.0010 |
| 10 | 1910 | 00:02:30 | 100.00% | | 0.0085 | | 0.0010 |
| 10 | 1920 | 00:02:31 | 100.00% | | 0.0072 | | 0.0010 |
| 10 | 1930 | 00:02:31 | 100.00% | | 0.0149 | | 0.0010 |
| 10 | 1940 | 00:02:32 | 100.00% | | 0.0155 | | 0.0010 |
| 10 | 1950 | 00:02:35 | 100.00% | 96.41% | 0.0190 | 0.2316 | 0.0010 |
|======================================================================================================================|
</pre><h2 id="9">Classify validation</h2><pre class="codeinput"> labels = classify(net_Wael,imdsValidation);
</pre><h2 id="10">Compute Accuracy</h2><pre class="codeinput"> YValidation = imdsValidation.Labels;
accuracy02 = sum(labels == YValidation)/numel(YValidation)*100;
dis=[<span class="string">'The final accuracy rate is: '</span>, num2str(accuracy02,<span class="string">'%2.2f'</span>), <span class="string">'%'</span>];
disp(dis);
diary <span class="string">off</span>
</pre><pre class="codeoutput">The final accuracy rate is: 97.16%
</pre><h2 id="11">move diary file</h2><pre class="codeinput">movefile(<span class="string">'Progress.txt'</span>,<span class="string">'results'</span>)
</pre><h2 id="12"><b>Test one at a time</b></h2><pre class="codeinput">true=0;
false=0;
<span class="keyword">for</span> i = 1:size(imdsValidation.Files,1)
<span class="keyword">if</span> labels(i) == imdsValidation.Labels(i)
true=true+1;
<span class="keyword">else</span>
false=false+1;
<span class="keyword">end</span>
<span class="keyword">end</span>
</pre><h2 id="13">Find the square root of the false matches to determine the subplots</h2><pre class="codeinput"> f0=floor(sqrt(false));
<span class="keyword">if</span> f0 == sqrt(false)
f1=f0;
<span class="keyword">else</span>
f1=f0+1;
<span class="keyword">end</span>
</pre><h2 id="14">Recalbirating true and false</h2><pre class="codeinput"> true=0;
false=0;
</pre><h2 id="15">start plotting mismatches</h2><pre class="codeinput"> falsefig = figure(<span class="string">'Name'</span>,<span class="string">'All Mismatch Pictures'</span>,<span class="string">'Visible'</span>,<span class="string">'off'</span>,<span class="string">'Units'</span>, <span class="string">'Normalized'</span>, <span class="string">'OuterPosition'</span>, [0, 0.04, 1, 0.96]);
<span class="keyword">for</span> i = 1:size(imdsValidation.Files,1)
imf = imread(imdsValidation.Files{i});
<span class="keyword">if</span> labels(i) ~= imdsValidation.Labels(i)
colorText = <span class="string">'r'</span>;
false=false+1;
<span class="keyword">if</span> false &gt; f0*f1
<span class="keyword">break</span>
<span class="keyword">else</span>
subplot(f1,f0,false);
imshow(imf);
title(char(labels(i)),<span class="string">'Color'</span>,colorText);
<span class="keyword">end</span>
<span class="keyword">end</span>
<span class="keyword">end</span>
xlabel(<span class="string">'ALL mismatch incidents'</span>);
saveas(falsefig,<span class="string">'All Mismatches.png'</span>);
movefile(<span class="string">'All Mismatches.png'</span>,<span class="string">'results'</span>);
</pre><h2 id="16">start plotting sample of correct match</h2><pre class="codeinput"> truefig = figure(<span class="string">'Name'</span>,<span class="string">'Sample of Correct Match'</span>,<span class="string">'Visible'</span>,<span class="string">'off'</span>,<span class="string">'Units'</span>, <span class="string">'Normalized'</span>, <span class="string">'OuterPosition'</span>, [0, 0.04, 1, 0.96]);
true=0;
false=0;
<span class="keyword">for</span> i = 1:size(imdsValidation.Files,1)
ii = randi(size(imdsValidation.Files,1));
imt = imread(imdsValidation.Files{ii});
<span class="keyword">if</span> labels(ii) == imdsValidation.Labels(ii)
colorText = <span class="string">'g'</span>;
true=true+1;
<span class="keyword">if</span> true &gt; f0*f1
<span class="keyword">break</span>
<span class="keyword">else</span>
subplot(f1,f0,true);
imshow(imt);
title(char(labels(ii)),<span class="string">'Color'</span>,colorText);
<span class="keyword">end</span>
<span class="keyword">end</span>
<span class="keyword">end</span>
xlabel(<span class="string">'Sample of Correct Incidents'</span>);
saveas(truefig,<span class="string">'Sample Matches.png'</span>);
movefile(<span class="string">'Sample Matches.png'</span>,<span class="string">'results'</span>);
</pre><p class="footer"><br><a href="https://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2020a</a><br></p></div><!--
##### SOURCE BEGIN #####
% Biometric Systems
% CYS616
% CONVOLUTIONAL NEURAL NETWORK FOR IRIS RECOGNITION
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
reset(gpuDevice); % Reset GPU memory
diary Progress.txt
diary on
%% Load train Data
categories = {
'001','002','003','004','005','006','007','008','009','010', ...
'011','012','013','014','015','016','017','018','019','020', ...
'021','022','023','024','025','026','027','028','029','030', ...
'031','032','033','034','035','036','037','038','039','040', ...
'041','042','043','044','045','046','047','048','049','050', ...
'051','052','053','054','055','056','057','058','059','060', ...
'061','062','063','064','065','066','067','068','069','070', ...
'071','072','073','074','075','076','077','078','079','080', ...
'081','082','083','084','085','086','087','088','089','090', ...
'091','092','093','094','095','096','097','098','099','100', ...
'101','102','103','104','105','106','107','108','109','110', ...
'111','112','113','114','115','116','117','118','119','120', ...
'121','122','123','124','125','126','127','128','129','130', ...
'131','132','133','134','135','136','137','138','139','140', ...
'141','142','143','144','145','146','147','148','149','150', ...
'151','152','153','154','155','156','157','158','159','160', ...
'161','162','163','164','165','166','167','168','169','170', ...
'171','172','173','174','175','176','177','178','179','180', ...
'181','182','183','184','185','186','187','188','189','190', ...
'191','192','193','194','195','196','197','198','199','200', ...
'201','202','203','204','205','206','207','208','209','210', ...
'211','212','213','214','215','216','217','218','219','220', ...
'221','222','223'};
imdsTrain = imageDatastore(fullfile(pwd,'Dataset/TrainData', categories),'IncludeSubfolders',true,'FileExtensions','.bmp','LabelSource','foldernames');
num_train = size(imdsTrain.Files,1);
%% Size of first image in dataset
img = readimage(imdsTrain,1);
[x , y , z] = size(img);
%% Load Test Data
imdsValidation = imageDatastore(fullfile(pwd,'Dataset/TestData', categories),'IncludeSubfolders',true,'FileExtensions','.bmp','LabelSource','foldernames');
num_test = size(imdsValidation.Files,1);
%% Calculate the number of images in each category.
labelCount = countEachLabel(imdsTrain);
%% Define Network Architecture
layers = [
imageInputLayer([x y z]);
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer();
maxPooling2dLayer(5,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer();
averagePooling2dLayer(5,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer();
maxPooling2dLayer(5,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer();
averagePooling2dLayer(5,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer();
fullyConnectedLayer(size(categories,2),'BiasLearnRateFactor',2);
softmaxLayer
classificationLayer];
%% Specify Training Options
options = trainingOptions('sgdm', ...
'InitialLearnRate', 0.001, ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',100, ...
'Shuffle','every-epoch', ...
'MaxEpochs', 10, ...
'MiniBatchSize', 8, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.5, ...
'LearnRateDropPeriod',50, ...
'ExecutionEnvironment','gpu', ...
'Verbose', true, 'VerboseFrequency', 10);
%% Train Network Using Training Data
[net_Wael, info] = trainNetwork(imdsTrain,layers,options);
save('TrainedNetwork.mat','net_Wael')
movefile('TrainedNetwork.mat','results')
%% Classify validation
labels = classify(net_Wael,imdsValidation);
%% Compute Accuracy
YValidation = imdsValidation.Labels;
accuracy02 = sum(labels == YValidation)/numel(YValidation)*100;
dis=['The final accuracy rate is: ', num2str(accuracy02,'%2.2f'), '%'];
disp(dis);
diary off
%% move diary file
movefile('Progress.txt','results')
%% *Test one at a time*
true=0;
false=0;
for i = 1:size(imdsValidation.Files,1)
if labels(i) == imdsValidation.Labels(i)
true=true+1;
else
false=false+1;
end
end
%% Find the square root of the false matches to determine the subplots
f0=floor(sqrt(false));
if f0 == sqrt(false)
f1=f0;
else
f1=f0+1;
end
%% Recalbirating true and false
true=0;
false=0;
%% start plotting mismatches
falsefig = figure('Name','All Mismatch Pictures','Visible','off','Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
for i = 1:size(imdsValidation.Files,1)
imf = imread(imdsValidation.Files{i});
if labels(i) ~= imdsValidation.Labels(i)
colorText = 'r';
false=false+1;
if false > f0*f1
break
else
subplot(f1,f0,false);
imshow(imf);
title(char(labels(i)),'Color',colorText);
end
end
end
xlabel('ALL mismatch incidents');
saveas(falsefig,'All Mismatches.png');
movefile('All Mismatches.png','results');
%% start plotting sample of correct match
truefig = figure('Name','Sample of Correct Match','Visible','off','Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
true=0;
false=0;
for i = 1:size(imdsValidation.Files,1)
ii = randi(size(imdsValidation.Files,1));
imt = imread(imdsValidation.Files{ii});
if labels(ii) == imdsValidation.Labels(ii)
colorText = 'g';
true=true+1;
if true > f0*f1
break
else
subplot(f1,f0,true);
imshow(imt);
title(char(labels(ii)),'Color',colorText);
end
end
end
xlabel('Sample of Correct Incidents');
saveas(truefig,'Sample Matches.png');
movefile('Sample Matches.png','results');
##### SOURCE END #####
--></body></html>
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