Pro-process to increase the face recognition rate

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Hi, I am doing the final year project for face recognition based attendance system.
I tried to increase the brightness of the photo and it did help me to improve the recognition rate a bit, so i wonder anyone can tell me what pre-process technique can be use to increase the recognition rate?
Thank :)

Accepted Answer

Image Analyst
Image Analyst on 1 Jun 2014
There are lots of things that could improve recognition such as looking straight into the camera, having eyes open and mouth closed and in a neutral position, having proper illumination (no shadows, same exposure as in database, etc.), etc. See this for lots more ideas: http://www.visionbib.com/bibliography/contentspeople.html#Face%20Recognition,%20Detection,%20Tracking,%20Gesture%20Recognition,%20Fingerprints,%20Biometrics
  6 Comments
Dalvin
Dalvin on 2 Jun 2014
ok i just using the face pic that i detected, but before using the regionprops(), i need to use im2bw() 1st right? after that, if i run the regionprops(), it return me many angle, so which angle should i pick? sorry im still a beginner to use matlab haha
omar A.alghafoor
omar A.alghafoor on 17 Mar 2021
Hi Mathworks team .
I am having two problems distinguishing faces using (face recognition convolutional neural network)
First: How to detect the intruder.
Second: The facial recognition overlaps between one person and another in the system.
The first test on grayscale images was good recognition, but on realtime of web camera the results are incorrect, knowing that I use a camera that has accuracy: 1024x570
note : all imge are grayscale .
Where is the defect in the code?
this my code for training dataset:
clc
clearvars
close all
%% variables
trainingNumFiles = 0.8;
rng(1)
faceData = imageDatastore('AutoCapturedFaces','IncludeSubfolders',true,'LabelSource','foldernames');
% Resize the images to the input size of the net
faceData.ReadFcn = @(loc)imresize(imread(loc),[227,227]);
% read one image to get pixel size
img = readimage(faceData,1);
% splitting the testing and training data
[trainFaceData,testFaceData] = splitEachLabel(faceData, ...
trainingNumFiles,'randomize');
%% defining CNN parameters
% defining layers
layers = [imageInputLayer([size(img,1) size(img,2) 1])
%middle layers
convolution2dLayer(5,3,'Padding', 2, 'Stride',3)
reluLayer
maxPooling2dLayer(3,'Stride',3)
%final layers
fullyConnectedLayer(8)
softmaxLayer
classificationLayer()];
% options to train the network
options = trainingOptions('sgdm', ...
'MiniBatchSize', 40, ...
'InitialLearnRate', 1e-4, ...
'MaxEpochs', 25, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.875, ...
'LearnRateDropPeriod', 12, ...
'VerboseFrequency', 5);
% training the network
convnet = trainNetwork(trainFaceData,layers,options);
%% classifying
YTest = classify(convnet,testFaceData);
TTest = testFaceData.Labels;
%% Calculate the accuracy.
accuracy = sum(YTest == TTest)/numel(TTest)
save convnet
accuracy =
0.9375
https://www.mathworks.com/matlabcentral/answers/774947-face-recognition-convolutional-neural-network?s_tid=prof_contriblnk

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More Answers (1)

tanzin angmo
tanzin angmo on 19 Apr 2017
i used pca algo to match test data with database. but the images with tilted faces are not getting recognized. is the algo a problem? help

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