face-recognition code in matlab

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Hello all ... all i need is face recognition and face matching code in matlab because my project depends on that and i've not studied matlab or DIP as a course .... please help me by giving me face recognition code in matlab ... thnxxxxx

回答(1 个)

Sabarinathan Vadivelu
If you have MATLAB R2013a, Then you can find the face detector which comes inbuilt with MATLAB.
% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();
% Read a video frame and run the detector.
videoFileReader = vision.VideoFileReader('visionface.avi');
videoFrame = step(videoFileReader);
bbox = step(faceDetector, videoFrame);
% Draw the returned bounding box around the detected face.
videoOut = insertObjectAnnotation(videoFrame,'rectangle',bbox,'Face');
figure, imshow(videoOut), title('Detected face');
  7 个评论
shahana muzaffar
shahana muzaffar 2015-8-19
编辑:shahana muzaffar 2015-8-19
if we want to capture the image through direct integrated cam wt can i do??plz tel me in 2014a matlab
omar A.alghafoor
omar A.alghafoor 2021-3-17
编辑:Walter Roberson 2023-3-30
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

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