GUI-FaceRecognition Using Matlab Report-JinJin

8/10/2019 GUI-FaceRecognition Using Matlab Report-JinJin

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GANNON UNIVERSITY

Electrical & Computer Engineering Department

Digital Image Processing

GUI-Face Recognition using PCA on MATLAB

Report Prepared by:

xxxxx

Instructorxxxxx

May.6.2014


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1. Summary

This is the summary of the basic idea about PCA and the papers about the face

recognition using PCA.

The Principal Component Analysis (PCA) is one of the most successful techniques

that have been used in image recognition and compression. PCA is a statistical

method under the broad title of factor analysis. The purpose of PCA is to reduce the

large dimensionality of the data space (observed variables) to the smaller intrinsic

dimensionality of feature space (independent variables), which are needed to describe

the data economically. This is the case when there is a strong correlation between

observed variables.The jobs which PCA can do are prediction, redundancy removal, feature extraction,

data compression, etc.

Because PCA is a classical technique which can do something in the linear domain,

applications having linear models are suitable, such as signal processing, image

processing, system and control theory, communications, etc.

Face recognition has many applicable areas. Moreover, it can be categorized into face

identication, face classication, or sex determination. The most useful applications

contain crowd surveillance, video content indexing, personal id entication (ex.

drivers license), mug shots matching, entrance security, etc.

The main idea of using PCA for face recognition is to express the large 1-D vector of

pixels constructed from 2-D facial image into the compact principal components of

the feature space. This can be called eigenspace projection.

Eigenspace is calculated by identifying the eigenvectors of the covariance matrix

derived from a set of facial images(vectors).

The details are described in the following section.


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2. Block diagram of the process

Face Input(Get from Face Database orFace Capture using Camera)

Face Verification (matching) Module

- Calculate PCA features of Database imageand Input image (using covariance matrix andeigenfaces)

- Database images resizing for matching withinput image

- Find correlation between two feature vectorsusing standard formula (norm)

Matching test withusing above correlation

Output:1. Read & output face image

from databse2. Green signal show

Output:- No face in

database- Red signal show

No

Yes


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3. Implementation

This projects is organized into 5 modules

3.1 Function to Load image from computer (Module1)

- MATLAB Codefunction pushbutton1_Callback(hObject, eventdata, handles)

% function to Load Image from computer option

[filename, user_canceled] = imgetfile; % ask user to select image

if user_canceledmsgbox( 'File not selected' , 'Error' ,'error' )

else

test_image=imread(filename); % read and load the selected image in matlab

axes(handles.axes1);

imshow(test_image) % display the test image

cla(handles.axes2); % clear previously opened images on GUI

cla(handles.axes3);

set(handles.text6, 'string' ,'') % display on GUI

end

- Function analysis in this module

Table 1. Module1 function description

Function name Descriptionimgetfile Open Image dialog boxmsgbox Create and open message box

imread Read image from graphics file

imshow Display image

cla Clear current axe


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set Set Handle Graphics object properties

rgb2gray Convert RGB image or colormap to grayscalesize Image resize (Array Dimensions)

3.2 function for matching algorithm (Module2)

- MATLAB Codeglobal sze I_test % global variables

A=PCA_features(I_test, 2); % calculate PCA features

A=A(:); % Make it feature array

database_images = 41; % number of images in database images

% loop based procedure to load each image from database sequentially (Broad Design Steps 1b)

for k=1:database_images

Imd=imread(strcat( 'Database_large\' ,num2str(k), '.jpg' )); % read database image

Im=rgb2gray(Imd); % RGB to gray conversion

I1=double(imresize(Im, sze));

% resizing database image same as test image (Broad Design Steps 2)

% matching stage

B=PCA_features(I1, 2);

B=B(:); % Feature array

% Find correlation between two feature vectors using

% standard formula, (Broad Design Steps 3)

corr(k) = sum(A.*B)/(norm(A)*norm(B));


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end

Max_Match=max(corr); % find maximum correlation

% Check if correlation >0.60 then match successful

if Max_Match>0.80

ind=find(corr==Max_Match);

Match_image=imread(strcat( 'Database_large\' ,num2str(ind), '.jpg' )); % read database

image

axes(handles.axes2); % axes handle for GUI

imshow(Match_image) % displaying the matched image

axes(handles.axes3); % 3rd axes control for displaying red/green LED

% display circle with green color in case we find a match

rectangle( 'Position' ,[1,2,0.5,1], 'Curvature' ,[1,1], 'FaceColor' ,'g')

% calculate confidence value with correlation 0.60 = 50% criterea

conf_value=abs((Max_Match-0.60))*60/50*100+50;

set(handles.text6, 'string' ,num2str(round(conf_value))); % display on GUI

else


% display red LED in case no match is found

rectangle( 'Position' ,[1,2,0.5,1], 'Curvature' ,[1,1], 'FaceColor' ,'r')

set(handles.text6, 'string' ,'--' ); % display on GUI

end



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Table 2. Module2 function descriptionFunction name Description

PCA_features This function extracts the PCA Features of the Input Image with 'k' newdimensionsk= eigen values reduction parameter

Corr Linear or tank correlation

Sum Sum of array elementsnorm Vector and matrix normsmax Largest elements in arrayFind Find indics and values of nonzero elementsaxes Create axes graphics objectRectangle Create 2-D rectangle object and showSet Set Handle Graphics properties

3.3 Function for camera set and control (Module 3)

- MATLAB Codeglobal c

c=1;


% figure,

vidObj = videoinput( 'winvideo' ,1); % For windows users, videoinput

% vidObj = videoinput('macvideo',1); % For Mac users, videoinput

set(vidObj, 'ReturnedColorSpace' , 'RGB' ); % set returned frame in RGB format

videoRes = get(vidObj, 'VideoResolution' );

numberOfBands = get(vidObj, 'NumberOfBands' );

handleToImage = image( zeros([videoRes(2), videoRes(1), numberOfBands], 'uint8' ) );

preview(vidObj, handleToImage);

handles.vidObj = vidObj; % add the video data to GUI handle

guidata(hObject,handles)



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Table 3. Module1 function description

Function name DescriptionVideoinput Create video input object

Get Query Handle Graphics object propertiesImage Display image objectPreview Preview of live video dataGuidata Store or retrieve GUI data

3.4 Function for Face snapshot on camera video (Module4)

- MATLAB Code

global sze I_test c

if c==1

sze=[512 512]; % set default size for image captured by webcam

frame = getsnapshot(handles.vidObj); % get snapshot from video

[H, W, a]=size(frame);

c1=0.2; % top and bottom clipping of snaphot image to get face area

c2=0.3; % left and right clipping of snaphot image to get face area

clip_H=round(c1*H);

clip_W=round(c2*W);

f1=frame(clip_H:H-clip_H, clip_W:W-clip_W,:); % clip the frame

axes=handles.axes1;

imshow(f1);

f1=rgb2gray(f1); % convert RGB image to gray scale image

I_test=double(imresize(f1,sze));


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% clear all previously opened data/images on GUI

cla(handles.axes2); % clear previously opened images on GUI

cla(handles.axes3);

set(handles.text6, 'string' ,'') % display on GUI

c=0;

else end


Table 4. Module4 function descriptionFunction name Description

getsnapshot Immediately return single image frameframe(clip_H:H-clip_H, clip_W:W-clip_W,:)

Clip the frame

3.5 Calculate PCA features of the Input Image & Test Image with k newdimensions (Module5)

- MATLAB codefunction features=PCA_features(I, k)

if numel(size(I))==3 I = rgb2gray(I);

end Id = im2double(I);

meen = mean(Id); [a ~] = size(Id); I_Adjust = Id - repmat(meen,a,1); % mean subtraction

cov_data = cov(I_Adjust); % covariance matrix

[feature_vector,~] = eigs(cov_data,k); % eigen values of covariance matrix

features=feature_vector'*I_Adjust'; % Final Data

- Function analysis


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4. Test and validation

Fig 1. Face Recognition Initial GUI

Case 1: Loading image from Computer

Fig 2. Load Image from Computer

The GUI will appear and looklike this


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The selected image will be displayed on the left panel as shown below.

Fig 3. Loaded image show on the left panel

The selected image will be displayed on the left panel as shown below. Now click on ExecuteMatching Algorithm.

Fig 4. Excute Face Matching Algorithm Button Click


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If the test image is in the database, itll be displayed in the next pane and green LED will bedisplayed. Match confidence will also be displayed.

Shown below is the case when a match is found and green LED is ON.

Fig 5. Matching result show (green: matching success!)

Shown below is the case when no match is found and red LED is ON.


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Fig 6. Matching result show (red: matching failed!)

Case 2: Capture image from webcam

To match a particular face, its similar image must be in database folder else it may not berecognized. For example, I put my photos in the database for its testing. The images weretaken and cropped so that only face area is included only.

Fig 7. Standard Captured Face Image from camera

To test the matching algorithm for input image taken by webcam.


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Fig 8. Capture Image from Camera button click for using Camera Capture

By pressing the button Capture Image from Camera, the webcam will be enabled anditll show video in the test image pane on the left. See figure below.

Fig 9. Camera video showing


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Capture button takes the snapshot and after cropping, takes the middle portion.

Fig 10. Face Capture from Camera by Capture button click

Now run the Execute Face Matching Algorithm and see the result as shown below.

Fig 11. Matching result show using Camera (green: matching success!)


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Note: For good matching results, you are required to pose your face in the mid-section of thevideo. The snapshot is cropped and only middle section is used for matching purpose. Do not take

snapshot while the face is not in middle portion. Please see the following example.

Fig 12. Face Capture guide where using camera

After capturing, the test image will not contain full face.

Fig. 13. Face Capture failed show(because you didnt face capture in capture windows middle)

Face not inmiddle


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5. Discussion and conclusions

In this paper we researched Face Recognition using PCA and implemented it in MATLAB.

We considered this program in image get from local computer and using camera capture.

The test result images show advantage and disadvantage of this algorithm.

In the future, we will good methods for Face Recognition.

Thanks.


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References

[1] M.A. Turk and A.P. Pentland, Face Recognition Using Eigenfaces, IEEE Conf. on

Computer Vision and Pattern Recognition, pp. 586-591, 1991.

[2] K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks: Theory andApplications, John Wiley & Sons,Inc., 1996.

[3] Alex Pentland, Baback Moghaddam, and Thad Starner, View -Based and Modular

Eigenspaces for Face Recognition, IEEE Conf. on Computer Vision and Pattern Recognition,

MITMedia Laboratory Tech. Report No. 245 1994


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Appendix- Face_Match.m

function varargout = Face_Match(varargin) % FACE_MATCH MATLAB code for Face_Match.fig % FACE_MATCH, by itself, creates a new FACE_MATCH or raises the existing % singleton*. %

% H = FACE_MATCH returns the handle to a new FACE_MATCH or the handle to % the existing singleton*. % % FACE_MATCH('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in FACE_MATCH.M with the given input arguments. % % FACE_MATCH('Property','Value',...) creates a new FACE_MATCH or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before Face_Match_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to Face_Match_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help Face_Match

% Last Modified by GUIDE v2.5 12-Apr-2014 13:44:56

% Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct( 'gui_Name' , mfilename, ...

'gui_Singleton' , gui_Singleton, ...

'gui_OpeningFcn' , @Face_Match_OpeningFcn, ... 'gui_OutputFcn' , @Face_Match_OutputFcn, ... 'gui_LayoutFcn' , [] , ... 'gui_Callback' , []);

if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});

end

if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else gui_mainfcn(gui_State, varargin{:});

end % End initialization code - DO NOT EDIT

% --- Executes just before Face_Match is made visible. function Face_Match_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to Face_Match (see VARARGIN)


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% Choose default command line output for Face_Match handles.output = hObject;

% Update handles structure guidata(hObject, handles); global c c=0; clc

% UIWAIT makes Face_Match wait for user response (see UIRESUME) % uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line. function varargout = Face_Match_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure varargout{1} = handles.output;

% --- Executes on button press in pushbutton1. function pushbutton1_Callback(hObject, eventdata, handles)

% function to Load Image from computer option

[filename, user_canceled] = imgetfile; % ask user to select image

if user_canceled

msgbox( 'File not selected' , 'Error' ,'error' ) else test_image=imread(filename); % read and load the selected image in matlab axes(handles.axes1); imshow(test_image) % display the test image

cla(handles.axes2); % clear previously opened images on GUI cla(handles.axes3); set(handles.text6, 'string' ,'') % display on GUI

end

global sze I_test c I_test=double(rgb2gray(test_image)); % convert RGB image to gray scale image sze=size(I_test); % size of test image c=0;

% --- Executes on button press in pushbutton2. function pushbutton2_Callback(hObject, eventdata, handles) % function for matching algorithm button

global sze I_test % global variables


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A=PCA_features(I_test, 2); % calculate PCA features A=A(:); % Make it feature array

database_images = 41; % number of images in database images

% loop based procedure to load each image from database sequentially (Broad Design Steps 1b) for k=1:database_images

Imd=imread(strcat( 'Database_large\' ,num2str(k), '.jpg' )); % read database image

Im=rgb2gray(Imd); % RGB to gray conversion

I1=double(imresize(Im, sze)); % resizing database image same as test image (Broad Design Steps 2)

% matching stage

B=PCA_features(I1, 2); B=B(:); % Feature array

% Find correlation between two feature vectors using % standard formula, (Broad Design Steps 3)

corr(k) = sum(A.*B)/(norm(A)*norm(B));

end

Max_Match=max(corr); % find maximum correlation % Check if correlation >0.60 then match successful

if Max_Match>0.80 ind=find(corr==Max_Match); Match_image=imread(strcat( 'Database_large\' ,num2str(ind), '.jpg' )); % read database image axes(handles.axes2); % axes handle for GUI imshow(Match_image) % displaying the matched image

axes(handles.axes3); % 3rd axes control for displaying red/green LED

% display circle with green color in case we find a match rectangle( 'Position' ,[1,2,0.5,1], 'Curvature' ,[1,1], 'FaceColor' ,'g')

% calculate confidence value with correlation 0.60 = 50% criterea conf_value=abs((Max_Match-0.60))*60/50*100+50;

set(handles.text6, 'string' ,num2str(round(conf_value))); % display on GUI

else axes(handles.axes3);

% display red LED in case no match is found rectangle( 'Position' ,[1,2,0.5,1], 'Curvature' ,[1,1], 'FaceColor' ,'r')

set(handles.text6, 'string' ,'--'); % display on GUI end


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% --- Executes on button press in pushbutton3. function pushbutton3_Callback(hObject, eventdata, handles) % Function for camera button global c c=1; axes(handles.axes1); % figure, vidObj = videoinput( 'winvideo' ,1); % For windows users, videoinput % vidObj = videoinput('macvideo',1); % For Mac users, videoinput

set(vidObj, 'ReturnedColorSpace' , 'RGB' ); % set returned frame in RGB format

videoRes = get(vidObj, 'VideoResolution' ); numberOfBands = get(vidObj, 'NumberOfBands' );

handleToImage = image( zeros([videoRes(2), videoRes(1), numberOfBands], 'uint8' ) );

preview(vidObj, handleToImage);

handles.vidObj = vidObj; % add the video data to GUI handle guidata(hObject,handles)

% --- Executes on button press in pushbutton5. function pushbutton5_Callback(hObject, eventdata, handles)

global sze I_test c if c==1

sze=[512 512]; % set default size for image captured by webcam frame = getsnapshot(handles.vidObj); % get snapshot from video

[H, W, a]=size(frame); c1=0.2; % top and bottom clipping of snaphot image to get face area c2=0.3; % left and right clipping of snaphot image to get face area clip_H=round(c1*H); clip_W=round(c2*W);

f1=frame(clip_H:H-clip_H, clip_W:W-clip_W,:); % clip the frame axes=handles.axes1; imshow(f1);

f1=rgb2gray(f1); % convert RGB image to gray scale image

I_test=double(imresize(f1,sze));

% clear all previously opened data/images on GUI cla(handles.axes2); % clear previously opened images on GUI cla(handles.axes3); set(handles.text6, 'string' ,'') % display on GUI

c=0; else end


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- PCA_features.m

%% This function extracts the PCA Features of the Input Image with 'k' new dimensions % k=eigen values reduction parameter

function features=PCA_features(I, k)

if numel(size(I))==3 I = rgb2gray(I);

end Id = im2double(I);

meen = mean(Id); [a ~] = size(Id); I_Adjust = Id - repmat(meen,a,1); % mean subtraction

cov_data = cov(I_Adjust); % covariance matrix

[feature_vector,~] = eigs(cov_data,k); % eigen values of covariance matrix

features=feature_vector'*I_Adjust'; % Final Data


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- Face_Match.fig (Our Face Recognition Proejcts GUI)

GUI-FaceRecognition Using Matlab Report-JinJin

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