Pro-process to increase the face recognition rate
2 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Dalvin
am 1 Jun. 2014
Kommentiert: omar A.alghafoor
am 17 Mär. 2021
Hi, I am doing the final year project for face recognition based attendance system.
I'm using the fisherface method [ http://www.mathworks.com/matlabcentral/fileexchange/17066-fld-based-face-recognition-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 :)
0 Kommentare
Akzeptierte Antwort
Image Analyst
am 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 Kommentare
omar A.alghafoor
am 17 Mär. 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
Weitere Antworten (1)
tanzin angmo
am 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
0 Kommentare
Siehe auch
Produkte
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!