CODE

 CODING With Explanation:-

Copy Code for Face detection on Image

Link:- https://drive.google.com/file/d/1zXRjESEJ1piXb3fOATRGEkbcuKu2TgG9/view?usp=sharing

Bold Letter is Coding Part.

% Face Detection Steps :-

% 1.Image labelling

% 2.Training

% 3.Testing

load('Facedata.mat');  

%  1. Load labelled data file ,which created through image labelling

Facedetect = selectLabels(gTruth,'Face');

% 2. Creating Variable Facedetect in which will store labels 'Face'

if isfolder(fullfile('TrainingData'))    

cd TrainingData

else 

mkdir TrainingData

end

addpath('TrainingData');

% 3. if else condition, if means 'if full name TrainingData exist ,locate that file ' else here if TrainingData Files doesnt exits Make one and 

%        add it to the MATLAB Path

trainingData = objectDetectorTrainingData(Facedetect,'SamplingFactor',1,'writeLocation','TrainingData');

% Make variable trainingData in which will store and passing Parameters like Facedetect that is labels , Sampling factor means Examples Face images

% if sampling factor is 2 than 2times negative images taken, Writing location = as ' TrainingData Folder'.

detector = trainACFObjectDetector(trainingData,'NumStages',20);

% detector is variable storing data of ACF Object Detector Neural network , Numstages= Number of Training stages,

% More stages like 10,20 takes long time to train but with higher %Accuracy

save('Detector.mat','detector');

%  saving Detector file , so once ACF detector trained ,it can be used to detect Faces 

rmpath('TrainingData');

% Saving detector file in TrainingData Folder  

% Upto this 13 lines of Code , It needs to run Only Once .

% once we have save Our Neural Network 'Detector.mat' file which detects faces . one Have saved in TrainingData folder ,

% So to use it whenever we just need to load it by specfying  its path 

% Now u can delete Above codes,above 13 line is only one time needed to run.

This final Code.

load('Detector.mat');

% Load Detector file , it is Pretrained Neural network for face detection

img = imread('‪C:\Users\ANKIT TIWARI\Desktop\Face detection UTUBE\anu.jpg');

% img is Variable , imread is function for reading Image.

[bboxes,scores] = detect(detector,img);

% bboxes = Bounding Boxes which surrounds Face -Rectangle Box

% Scores = Confidence that is how sure a Detector is for identifying Human Face

for i = 1:length(scores)

For loop when i=1:Scores,

annotation = sprintf('Confidence = %.1f',scores(i));

%annotation is labels like face and Confidence in percentage

img = insertObjectAnnotation(img,'rectangle',bboxes(i,:),annotation);

end

figure

imshow(img);

Face  Tracking CODE:- 

LINK :-

https://drive.google.com/file/d/1-0aEqALETB5Xiv9T5SbK_RHHGmVCoExI/view?usp=sharing

 load('Facedata.mat');

 %  1. Load labelled data file ,which created through image labelling

Facedetect = selectLabels(gTruth,'Face');

% 2. Creating Variable FaceGtruth in which will store labels 'Face'

if isfolder(fullfile('TrainingData'))

cd TrainingData

else 

mkdir TrainingData

end

addpath('TrainingData');

% 3. if else condition, if means 'if full name TrainingData exist ,locate that file ' else here if TrainingData Files doesnt exits Make one and 

%        add it to the MATLAB Path

trainingData = objectDetectorTrainingData(Facedetect,'SamplingFactor',1,'writeLocation','TrainingData');

detector = trainACFObjectDetector(trainingData,'NumStages',5);

save('Detector.mat','detector');

rmpath('TrainingData');

% Make variable trainingData in which will store and passing Parameters like FaceGtruth that is labels , Sampling factor means Examples Face images

% if sampling factor is 2 than 2times negative images taken, Writing location = as TrainingData Folder

load('Detector.mat');

% load Trained Object Detector

vidReader = VideoReader('C:\Users\ANKIT TIWARI\Desktop\Face detection UTUBE\face Rawdata\MEDface.mp4');

% VideoReader is for reading Video

vidPlayer = vision.DeployableVideoPlayer;

% Play Video in MATLAB

i = 1;

results = struct('Boxes',[],'Scores',[]);

% results is variable for storing bounding box i.e rectangle values

while(hasFrame(vidReader))

% While loop  

I = readFrame(vidReader);

[bboxes, scores] = detect(detector,I,'Threshold',1);

[~,idx] = max(scores);

results(i).Boxes = bboxes;

results(i).Scores = scores;

annotation = sprintf('% , Confidence %4.2f' ,detector.ModelName,scores(idx));

I = insertObjectAnnotation(I,'rectangle',bboxes(idx,:),annotation);

step(vidPlayer,I);

i =i+1;

end

results = struct2table(results);