REAL TIME IMAGE PROCESSING FACE RECOGNITION FOR SECURITY SYSTEM
By
Tri Randi Uetama 2-1852-007
MASTER’S DEGREE in
MASTER OF MECHANICAL ENGINEERING
FACULTY OF ENGINEERING AND INFORMATION TECHNOLOGY
SWISS GERMAN UNIVERSITY The Prominence Tower
Jalan Jalur Sutera Barat Kav 15, Alam Sutera Kota Tangerang, Banten 15143
Indonesia
July, 2020
Revision After Thesis Defense on 17 July 2020
STATEMENT BY THE AUTHOR
I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.
Tri Randi Uetama
_____________________________________________
Student Date
Approved by:
Dr. Ir. Widi Setiawan
_____________________________________________
Thesis Advisor Date
Edi Sofyan, B.Eng., M.Eng., Ph.D
_____________________________________________
Thesis Co-Advisor Date
Dr. Maulahikmah Galinium S.Kom., M.Sc.,
_____________________________________________
Dean Date
ABSTRACT
REAL TIME IMAGE PROCESSING FACE RECOGNITION SYSTEM TO OPEN DOOR FOR SECURITY SYSTEM
By
Tri Randi Uetama Dr. Ir. Widi Setiawan, Advisor
Edi Sofyan, B.Eng., M.Eng., Ph.D, Co-Advisor SWISS GERMAN UNIVERSITY
This research presents how the human face can be recognized and used to control the opening of a door lock. The created system mainly consists of Arduino microcontoller based hardware and neural network based algorithms. The system has been fully assembled and successfully tested.
By using two different methods the point feature detector (PFD) method was used as the first method. An Eigen Feature function was utilized to detect feature point of image. The second method is convolutional neural network (CNN) to recognize human face. Using PFD method, a classification value has been setup <11. The classification value is used as classification category of the program to recognize the subject (face image) correctly. By using PFD method, the response of the system from starting of a face image recognition until opening the locker is 20 second. The CNN method used alexnet to classify the image. At least around 300 training input data are use per person. The face recognition’s experiment reached a high recognition’s accuracy of 99.99% level and an average response time of 10 seconds.
Keywords: Eigen Feature, Feature Point Detector, Convolution Neural Network, Alexnet, Classification Value.
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© Copyright 2020 by Tri Randi Uetama
All rights reserved
DEDICATION
I dedicated this work for future of my University and my country that I loved : Indonesia
ACKNOWLEDGEMENTS
This study is dedicated with wholeheartedly to my parents and all of my family who have been gave me motivation to achieve more acknowledge and gave me an inspiration and strength. I also thank to Dr. Ir. Widi Setiawan as my advisor who always support and give an advice regarding this thesis and also thank to Edi Sofyan, B.Eng., M.Eng., Ph.D, as my co-advisor who always give me an inspiration to solve many of my problem in this study.
TABLE OF CONTENTS
Page
STATEMENT BY THE AUTHOR ...2
ABSTRACT ...3
DEDICATION ...5
ACKNOWLEDGEMENTS ...6
TABLE OF CONTENTS ...7
LIST OF FIGURES ...9
LIST OF TABLES ...11
CHAPTER 1 - INTRODUCTION ...12
1.1 Research Problems ...13
1.2 Research Objectives ...13
1.3 Significance Of Study ...13
1.4 Research Questions ...13
1.5 Hypothesis...13
CHAPTER 2 – LITERATURE REVIEW ...14
2.1 Theoretical Perspective ...14
2.1.1 Image...14
2.1.2 Image Processing and Pattern Recognition ...14
2.1.2.1 One Dimentional Signal ...15
2.1.2.2 Two Dimentional Signal ...15
2.1.2.3 Three Dimentional Signal ...15
2.1.2.4 Digital Image Processing ...15
2.1.2.5 Element Of An Image Processing ...16
2.1.3 Neural Network ...16
2.1.3.1 Basic Of Neural Network...17
2.1.3.2 Neural Network Topology ...19
2.1.5 Multilayer Perceptron ...19
2.1.5.1 Perceptron Model ...19
2.1.6.1 Binary Classification ...20
2.1.7 Convolution Neural Network ...21
2.1.8 Convolution Layer ...22
2.1.9 Arduino Hardware ...22
2.1.9.1 Arduino Microcontroller Specification ...22
2.1.9.2 Arduino Mega2560 memory ...23
2.1.9.3 Arduino Communication ...24
2.1.9.4 Input and Output ...24
2.1.10 MATLAB and Arduino ...24
2.1.10.1 Connecting The Arduino Board To MATLAB ...24
2.2 Previoue Studies ...25
CHAPTER 3 – RESEARCH METHODOLOGY ...26
3.1 Materials and Equipment ...26
3.2 Software Design ...28
3.2.1 Face Recognition Base On PFD Method Block Diagram ...28
3.2.2 Algorithm Design Of Face Recognition Base On PFD ...29
3.2.3 Face Recognition Base On CNN Method Block Diagram ...39
3.2.3 Algorithm Design Of face Recognition Base On CNN Method...39
CHAPTER 4 – RESULTS AND DISCUSSIONS...49
4.1 Performance Test Face Recognition Of PFD Method ...49
4.1.1 Expeeriment 1: Adjusting The Resolution Of The Image ...49
4.1.2 Expeeriment 2: Adding More Subject Data ...49
4.1.3 Expeeriment 3: Using A Picture In The Front Of The Camera As Subject………50
4.1.4 Expeeriment 4: Reduce Loop Iteration ...50
4.1.5 Expeeriment 5: Recognizing Face With Different Angle ...51
4.2 Performance Test Convolution Neural Network Method ...51
4.2.1 Expeeriment 1: Adjusting The Resolution Of The Image ...52
4.2.2 Expeeriment 2: Adding More Subject Data ...53
4.2.3 Expeeriment 3: Using A Picture In The Front Of The Camera As Subject ...54
4.2.4 Expeeriment 4: Recognizing Face With Different Angle ...55
CHAPTER 5 – CONCLUSION AND RECCOMENDATIONS ...56
5.1 Conclusion ...56
5.2 Recommendations ...57
GLOSSARY ...58
REFERENCES ...59
CURRICULUM VITAE ...61
LIST OF FIGURES Figures Page 1. (a) Original picture; (b) gradient image; (c) connected feature points;(d) reconstructed drawing……….12
2. Image digitization ... 16
3. Digital image and numerical representation ... 16
4. Neuron model of McCulloch and Pitts’ ... 17
5. (a) Acyclic topology, (b) cyclic topology ... 19
6. Perceptron neural model ... 20
7. A three-layer MLP configuration ... 20
8. Binary Classification ... 21
9. Typical architecture of CNN ... 21
10. Convolution layer process... 22
11. Arduino Mega2560 ... 23
12. MATLAB command window ... 25
13. MATLAB command window ... 25
14. System flow process ... 26
15. Circuit diagram ... 27
16. Hardware view ... 28
17. Software block diagram ... 28
18. Storing the image data flow chart ... 29
19. Recognizing the image flow chart ... 37
21. Comparing image ... 38
22. Output result... 38
23. Face recognition base on CNN method block diagram ... 39
24. CNN training flow chart ... 43
25. CNN image prediction flow chart. ... 46
26. Capturing image and store it in memory... 47
27. Alexnet graph when calculate and learning the image data ... 47
28. Capture the image from camera to analyze in alexnet tool. ... 48
29. The alexnet output result ... 48
30. Alexnet’s layers ... 53
LIST OF TABLES
Table Page
1. Common image formats and properties……….……...14
2. Summary of net function……….……...18
3. Summary of activation function………18
4. Output changing vs image resolution input value……….49
5. Data recognizing face with more than one person………50
6. Subject recognition experiment result by using photo or a picture………...50
7. Time response vs output result when number of loop is changing………...51
8. Time response result when system when the face of human set up with different angle…….51
9. Output changing vs image resolution input value……….52
10. Data recognizing face with more than one person………..53
11. Subject recognition experiment result by using photo or a picture………...54
12. Time response result when system when the face of human set up with different angle…...51
