Artificial Neural Networks based Face Recognition

Hellooo Friend,

i need other help only if u can

im doing my final year project and im going out of my time if could please tell me any project u referred under topic

"Artificial Neural Networks based Face Recognition "

or any help ThankX

Answer 1

[Walter Roberson]

Are you asking for suggestions on projects that could be done on that topic? Or are you asking that people pay attention to your earlier posting, http://www.mathworks.com/matlabcentral/answers/6088-could-you-please-help-on-this-project that gave people next to no information?

[omar A.alghafoor]

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