Part1 Some Remain'.docx
This document was uploaded by user and they confirmed that they have the permission to share it. If you are author or own the copyright of this book, please report to us by using this DMCA report form. Report DMCA
Overview
Download & View Part1 Some Remain'.docx as PDF for free.
More details
- Words: 1,122
- Pages: 9
HAND GESTURE RECOGNITION SYSTEM FOR DUMB PEOPLE
by AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Submitted to the Department of Computer Science and Engineering in partial fulfillment of the requirements for the degree of Bachelor of Technology in Computer Science and Engineering
Krishna Engineering College Dr. A.P.J. Abdul Kalam Technical University, Lucknow May, 2017
DECLARATION I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, 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 of the university or other institute of higher learning, except where due acknowledgment has been made in the text.
AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Date:
ii
CERTIFICATE This is to certify that Project Report entitled “Hand Gesture Recognition System For Dumb People” which is submitted by Amit Singh (1316110024),Ankit Choudhary (1316110031), Arun Kumar Attri (1316110045), Ashutosh Singh (1316110052) in complete fulfillment of the requirement for the award of degree B. Tech. in Department of Computer Science and Engineering of Dr. A.P.J. Abdul Kalam Technical University, is a record of the candidate own work carried out by him under my supervision. The matter embodied in this is original and has not been submitted for the award of any other degree.
Date:
Supervisor Dr. Dileep Yadav
iii
ACKNOWLEDGEMENT It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during B. Tech. Final Year. We owe special debt of gratitude to Assistant Professor, Dr. Dileep Yadav Department of Computer Science & Engineering, Krishna Engineering College, Ghaziabad and for his constant support and guidance throughout the course of our work. His sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only his cognizant efforts that our endeavors have seen light of the day. We also take the opportunity to acknowledge the contribution of Professor Dr. Mayank Singh, Head, Department of Computer Science & Engineering, Krishna Engineering College, Ghaziabad for his full support and assistance during the development of the project. We also do not like to miss the opportunity to acknowledge the contribution of all faculty members of the department for their kind assistance and cooperation during the development of our project. Last but not the least, we acknowledge our friends for their contribution in the completion of the project.
AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Date:
iv
ABSTRACT In our country around 2.78% of peoples are not able to speak (dumb). Their communications with others are only using the motion of their hands and expressions. We proposed a new technique called artificial speaking mouth for dumb people. It will be very helpful to them for conveying their thoughts to others. Some peoples are easily able to get the information from their motions. The remaining is not able to understand their way of conveying the message. In order to overcome the complexity the artificial mouth is introduced for the dumb peoples. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it. A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures. Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.
v
TABLE OF CONTENTS
Page
DECLARATION
ii
CERTIFICATE
iii
ACKNOWLEDGEMENTS
iv
ABSTRACT
v
LIST OF TABLES
vii
LIST OF FIGURES
viii
LIST OF SYMBOLS
ix
LIST OF ABBREVIATIONS
x
CHAPTER 1 - INTRODUCTION
1
1.1 Problem Statement
2
1.2 Problem Definition
3
1.3 Expected Outcomes
3
CHAPTER 2 - LITERATURE SURVEY
7
CHAPTER 3 - PROPOSED METHODOLOGY
17
3.1 Project Constraintss
17
3.2 The Web Cam System
18
3.3 Brief Outline Of The Implementation System
19
3.4 ADVANTAGES
36
3.5 REQUIREMENT SPECIFICATION
37
3.5.1 Hardware and Software Requirement
37
CHAPTER 4 - FEATURE EXTRACTIONS
26
4.1 NEURAL NETWORKS
26
4.2 Row Vector Algorithm
28
4.3 Results of Concentration Analysis
42
CHAPTER 5 - CONCLUSION
43
REFERENCES
46
APPENDIX
48
LIST OF TABLE
Table 4.1 Table for Average Time per Matching
Page
42 vi
LIST OF FIGURES
Page
Figure 2.1 LIGHTING CONDITION AND BACKGROUND
4
Figure 2.2 HAND GESTURE RECOGNITION FLOW CHART
5
Figure 2.3 THE EFFECT OF SELF SHADOWING (A) AND CAST SHADOWING (B)
10
Figure 2.4 SYSTEM IMPLEMENTATION
23
Figure 3.1 DIFFERENT ETHNIC GROUP SKIN PATCHES
25
Figure 3.2 REMOVAL OF BACKGROUND
26
Figure 3.3 LABELING SKIN REGION
27
Figure 3.4 REAL TIME CLASSIFICATION
28
Figure 3.5 NEURAL NETWORK DIGRAM
29
Figure 3.6 NEURAL NET BLOCK DIGRAM
30
Figure 3.7 NN FOR ROW VECTOR AND EDGINE VECTOR
31
Figure 3.8 NN FOR MEAN STANDARD DEVIATION
32
Figure 3.10 ROW VECTOR FLOW CHART
33
Figure 3.11 DFD Level 0 – Automated Attendance System
32
Figure 3.12 DFD Level 1 – Student Face Registration Module
33
Figure 3.13 DFD Level 1 – Student Face Recognition Module
34
Figure 3.14 DFD Level 1 – Concentration Analysis Module
35
Figure 4.1 EDGING AND ROW VECTOR FLOW CHART
39
Figure 4.2 MEAD NAD SD FLOW CHART
39
Figure 4.3 DIAGONAL SUM
40
Figure 4.4 DIAGONAL SUM FLOW CHART Figure 4.5 GRAPHIC USER INTERFACE
40 41
Figure 4.6 PERFORMANCE CHART Figure 4.7 GRAPHICAL USER INTERFACE OUTPUT Figure 4.6 PERFORMANCE PERCENTAGE Figure 4.6 DEGREE OF ROTATION CLASSIFICATION Figure 4.6 DEGREE OF ROTATION MISCALSSIFICATION Figure 4.6 ETHNIC GROUP SKIN DETECTION Figure 4.6 CLASSIFICATION PERCENTAGE
42 vii
LIST OF SYMBOLS ∑
Summation
∫
Integrate
Belongs to
α
Alpha
ψ
Psi
Ф
Phi
λ
Lambda
μ
Mu
√
Square root
Ω
Omega
Δ
Delta
Θ
Theta
viii
LIST OF ABBREVIATIONS
CQ
Concentration Quotient
DFD
Data Flow Diagram
HDD
Hard Disk Drive
PC
Personal Computer
PCA
Principal Component Analysis
RAM
Random Access Memory
ix
by AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Submitted to the Department of Computer Science and Engineering in partial fulfillment of the requirements for the degree of Bachelor of Technology in Computer Science and Engineering
Krishna Engineering College Dr. A.P.J. Abdul Kalam Technical University, Lucknow May, 2017
DECLARATION I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, 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 of the university or other institute of higher learning, except where due acknowledgment has been made in the text.
AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Date:
ii
CERTIFICATE This is to certify that Project Report entitled “Hand Gesture Recognition System For Dumb People” which is submitted by Amit Singh (1316110024),Ankit Choudhary (1316110031), Arun Kumar Attri (1316110045), Ashutosh Singh (1316110052) in complete fulfillment of the requirement for the award of degree B. Tech. in Department of Computer Science and Engineering of Dr. A.P.J. Abdul Kalam Technical University, is a record of the candidate own work carried out by him under my supervision. The matter embodied in this is original and has not been submitted for the award of any other degree.
Date:
Supervisor Dr. Dileep Yadav
iii
ACKNOWLEDGEMENT It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during B. Tech. Final Year. We owe special debt of gratitude to Assistant Professor, Dr. Dileep Yadav Department of Computer Science & Engineering, Krishna Engineering College, Ghaziabad and for his constant support and guidance throughout the course of our work. His sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only his cognizant efforts that our endeavors have seen light of the day. We also take the opportunity to acknowledge the contribution of Professor Dr. Mayank Singh, Head, Department of Computer Science & Engineering, Krishna Engineering College, Ghaziabad for his full support and assistance during the development of the project. We also do not like to miss the opportunity to acknowledge the contribution of all faculty members of the department for their kind assistance and cooperation during the development of our project. Last but not the least, we acknowledge our friends for their contribution in the completion of the project.
AMIT SINGH
(1316110024)
ANKIT CHOUDHARY
(1316110031)
ARUN KUMAR ATTRI
(1316110045)
ASHUTOSH SINGH
(1316110052)
Date:
iv
ABSTRACT In our country around 2.78% of peoples are not able to speak (dumb). Their communications with others are only using the motion of their hands and expressions. We proposed a new technique called artificial speaking mouth for dumb people. It will be very helpful to them for conveying their thoughts to others. Some peoples are easily able to get the information from their motions. The remaining is not able to understand their way of conveying the message. In order to overcome the complexity the artificial mouth is introduced for the dumb peoples. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it. A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures. Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.
v
TABLE OF CONTENTS
Page
DECLARATION
ii
CERTIFICATE
iii
ACKNOWLEDGEMENTS
iv
ABSTRACT
v
LIST OF TABLES
vii
LIST OF FIGURES
viii
LIST OF SYMBOLS
ix
LIST OF ABBREVIATIONS
x
CHAPTER 1 - INTRODUCTION
1
1.1 Problem Statement
2
1.2 Problem Definition
3
1.3 Expected Outcomes
3
CHAPTER 2 - LITERATURE SURVEY
7
CHAPTER 3 - PROPOSED METHODOLOGY
17
3.1 Project Constraintss
17
3.2 The Web Cam System
18
3.3 Brief Outline Of The Implementation System
19
3.4 ADVANTAGES
36
3.5 REQUIREMENT SPECIFICATION
37
3.5.1 Hardware and Software Requirement
37
CHAPTER 4 - FEATURE EXTRACTIONS
26
4.1 NEURAL NETWORKS
26
4.2 Row Vector Algorithm
28
4.3 Results of Concentration Analysis
42
CHAPTER 5 - CONCLUSION
43
REFERENCES
46
APPENDIX
48
LIST OF TABLE
Table 4.1 Table for Average Time per Matching
Page
42 vi
LIST OF FIGURES
Page
Figure 2.1 LIGHTING CONDITION AND BACKGROUND
4
Figure 2.2 HAND GESTURE RECOGNITION FLOW CHART
5
Figure 2.3 THE EFFECT OF SELF SHADOWING (A) AND CAST SHADOWING (B)
10
Figure 2.4 SYSTEM IMPLEMENTATION
23
Figure 3.1 DIFFERENT ETHNIC GROUP SKIN PATCHES
25
Figure 3.2 REMOVAL OF BACKGROUND
26
Figure 3.3 LABELING SKIN REGION
27
Figure 3.4 REAL TIME CLASSIFICATION
28
Figure 3.5 NEURAL NETWORK DIGRAM
29
Figure 3.6 NEURAL NET BLOCK DIGRAM
30
Figure 3.7 NN FOR ROW VECTOR AND EDGINE VECTOR
31
Figure 3.8 NN FOR MEAN STANDARD DEVIATION
32
Figure 3.10 ROW VECTOR FLOW CHART
33
Figure 3.11 DFD Level 0 – Automated Attendance System
32
Figure 3.12 DFD Level 1 – Student Face Registration Module
33
Figure 3.13 DFD Level 1 – Student Face Recognition Module
34
Figure 3.14 DFD Level 1 – Concentration Analysis Module
35
Figure 4.1 EDGING AND ROW VECTOR FLOW CHART
39
Figure 4.2 MEAD NAD SD FLOW CHART
39
Figure 4.3 DIAGONAL SUM
40
Figure 4.4 DIAGONAL SUM FLOW CHART Figure 4.5 GRAPHIC USER INTERFACE
40 41
Figure 4.6 PERFORMANCE CHART Figure 4.7 GRAPHICAL USER INTERFACE OUTPUT Figure 4.6 PERFORMANCE PERCENTAGE Figure 4.6 DEGREE OF ROTATION CLASSIFICATION Figure 4.6 DEGREE OF ROTATION MISCALSSIFICATION Figure 4.6 ETHNIC GROUP SKIN DETECTION Figure 4.6 CLASSIFICATION PERCENTAGE
42 vii
LIST OF SYMBOLS ∑
Summation
∫
Integrate
Belongs to
α
Alpha
ψ
Psi
Ф
Phi
λ
Lambda
μ
Mu
√
Square root
Ω
Omega
Δ
Delta
Θ
Theta
viii
LIST OF ABBREVIATIONS
CQ
Concentration Quotient
DFD
Data Flow Diagram
HDD
Hard Disk Drive
PC
Personal Computer
PCA
Principal Component Analysis
RAM
Random Access Memory
ix
Related Documents
Part1 Some Remain'.docx
November 2019 11
Part1
October 2019 80
Part1
July 2020 20
Some
December 2019 34
Some
October 2019 27
Some
May 2020 16More Documents from "Marcos Paulo"
Part1 Some Remain'.docx
November 2019 11
Dx00449.xlsx
November 2019 11
Final Report (part-3).docx
November 2019 55
Introduction -priish.docx
November 2019 9
Poem Pkmkb.txt
December 2019 6