TABLE OF CONTENT
Cover page
Title page
Approval page
Dedication
Acknowledgment
Table of content
Abstract
CHAPTER ONE
1.0 Introduction
1.1 Background of the research
1.2 Statement of research problem
1.3 Objectives of the study
1.4 Significance of the study
1.5 Scope of the study
1.6 Limitation of the study
1.7 Definition of terms
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction
2.1 Review of concept
2.2 Review of related work
2.3 Empirical Study
2.4 System Architectural Framework/Structure
2.5 Summary of the review
CHAPTER THREE: SYSTEM ANALYSIS AND DESIGN
3.1 Introduction
3.5 Problem of the existing system
3.2 Method of data collection
3.3 Data preparation
3.4 Program structure
3.6 Justification for the new system
3.7 System modeling
3.8 System flow chat
3.9 Activity diagram
3.10 Program flow chart
3.11 Database specification and design
CHAPTER FOUR: SYSTEM TESTING AND DOCUMENTATION
4.1 Introduction
4.2 Program language justification
4.3 Systems requirement
4.4 Implementation details
4.5 Procedure testing plan
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion
5.3 Recommendation
Bibliography
Source code
CHAPTER ONE
1.0 INTRODUCTION
Face recognition system is an application for identifying someone from image or videos. Face recognition is classified into three stages ie)Face detection,Feature Extraction ,Face Recognition. Face detection method is a difficult task in image analysis. Face detection is an application for detecting object, analyzing the face, understanding the localization of the face and face recognition.It is used in many application for new communication interface, security etc.Face Detection is employed for detecting faces from image or from videos. The main goal of face detection is to detect human faces from different images or videos.The face detection algorithm converts the input images from a camera to binary pattern and therefore the face location candidates using the AdaBoost Algorithm. The proposed system explains regarding the face detection based system on AdaBoost Algorithm . AdaBoost Algorithm selects the best set of Haar features and implement in cascade to decrease the detection time .The proposed System for face detection is intended by using Verilog and ModelSim,and also implemented in FPGA.
Face Detection System is to detect the face from image or videos. To detect the face from video or image is gigantic. In face recognition system the face detection is the primary stage. Figure 1 shows the various stages of face recognition system ie face detection, feature extraction and recognition. Now Face Detection is in vital progress in the real world
Face recognition is a pattern recognition technique and one of the most important biometrics; it is used in a broad spectrum of applications. The accuracy is not a major problem that specifies the performance of automatic face recognition system alone, the time factor is also considered a major factor in real time environments. Recent architecture of the computer system can be employed to solve the time problem, this architecture represented by multi-core CPUs and many-core GPUs that provide the possibility to perform various tasks by parallel processing. However, harnessing the current advancements in computer architecture is not without difficulties. Motivated by such challenge, this research proposes a Face Detection and Recognition System (FDRS). In doing so, this research work provides the architectural design, detailed design, and four variant implementations of the FDRS.
1.1 BACKGROUND OF THE RESEARCH
Face recognition has gained substantial attention over in past decades due to its increasing demand in security applications like video surveillance and biometric surveillance. Modern facilities like hospitals, airports, banks and many more another organizations are being equipped with security systems including face recognition capability. Despite of current success, there is still an ongoing research in this field to make facial recognition system faster and accurate. The accuracy of any face recognition system strongly depends on the face detection system. The stronger the face detection system the better the recognition system would be. A face detection system can successfully detect human face from a given image containing face/faces and from live video involving human presence. The main methods used in these days for face detection are feature based and image based. Feature based method separates human features like skin color and facial features whereas image based method used some face patterns and processed training images to distinguish between face and non faces. Feature based method has been chosen because it is faster than image based method and its’ implementation is far more simplified. Face detection from an image is achieved through image processing. Locating the faces from images is not a trivial task; because images not just contain human faces but also non-face objects in clutter scenes. Moreover, there are other issues in face recognition like lighting conditions, face orientations and skin colors. Due to these reasons, the accuracy of any face recognition system cannot be 100%.
Face recognition is one of the most important biometrics methods. Despite the fact that there are more reliable biometric recognition techniques such as fingerprint and iris recognition, these techniques are intrusive and their success depends highly on user cooperation. Therefore, face recognition seems to be the most universal, non-intrusive, and accessible system. It is easy to use, can be used efficiently for mass scanning, which is quite difficult, in case of other biometrics . Also it is natural and socially accepted.
Moreover, technologies that require multiple individuals to use the same equipment to capture their biological characteristics probably expose the user to the transmission of germs and impurities from other users. However, face recognition is completely non-intrusive and does not carry any such health dangers.
Biometrics is a rapidly developing branch of information technology. Biometric technologies are automated methods and means for identification based on biological and behavioral characteristics of an individual. There are several advantages of biometric technologies compared to traditional identification methods. To take adequate measures against increasing security risks in modern world, countries are considering these advantages and are shifting to new generation identification systems based on biometric technologies.
1.2 STATEMENT OF RESEARCH PROBLEM
Biometric systems are becoming an important element (gateway) for information security systems. Therefore biometric systems themselves have to satisfy high security requirements. Unfortunately producers of biometric technologies do not always consider security precautions. In publications regarding biometric technologies, drawbacks and weaknesses of these technologies have been discussed. Since biometrics form the technology basis for large scale and very sensitive identification systems (e.g. passports, identification cards), the problem of adequate evaluation of the security of biometric technologies is a current issue.
Also, some other issues with face detection and recognition system is on individual with identical face like identical twins and others, in situation like this it is possible for the system to make mistake or error in processing the person image so as to grant access to the rightful user.
1.3 OBJECTIVES OF THE STUDY
The objective of this project is to implement a face recognition system which first detects the faces present in either single image frames; and then identifies the particular person by comparing the detected face with image database or in the both image frames.
In addition to the main objective of this research work, the researcher also went far more to add other features to the new system which are as fellow.
- One of the objectives of this system is to design a system that will help the organization maintain a strong security in the work environment.
- Highlight areas of vulnerability in the new system
- Develop a ridged and secure database for the organization to enable them secure their sensitive data and records.
1.4 SIGNIFICANCE OF THE STUDY
This study is primarily aimed at increasing efficiency in security, this research work will help the users in maintaining data. This system will reduce the rate of fraudulent activities as it can as well keep track of registered users and grant them access upon face recognition completion.
Also the knowledge that would be obtained from this research will assist the management to grow, also this research work will also be of help to the upcoming researcher in this field of study both with the academic students on their study.
1.5 SCOPE OF THE STUDY
The scope of this study covers only on face detection and recognition, accessing previous records and making matched for the data, updating of records and making delete.
1.6 LIMITATION OF THE STUDY
Many limitations encountered, were in the process of gathering information for the development of this project work to this extent. It was not an easy one, so many constraints were encountered during the collection of data.
The limitation focuses of the following constraints;
- FINANCIAL CONTRAINTS: the cost of sourcing for information and data that are involved in this work is high in the sense that we all know that information is money.
- TIME: A lot of time was involved in writing and developing this work,
iii. Irregularities in power supply also dealt harshly with the researcher.
1.7 DEFINITION OF TERMS
Analysis: Breaking a problem into successively manageable parts for individual study.
Attribute: A data item that characterize an object
Data flow: Movement of data in a system from a point of origin to specific destination indicated by a line and arrow
Data Security: Protection of data from loss, disclosure, modification or destruction.
Design: Process of developing the technical and operational specification of a candidate system for implements.
File: Collection of related records organized for a particular purpose also called dataset.
Flow Chart: A graphical picture of the logical steps and sequence involved in a procedure or a program.
Form: A physical carrier of data of information
Implementation: In system development-phase that focuses on user training, site preparation and file conversion for installing a candidate system.
Maintenance: Restoring to its original condition
Normalization: A process of replacing a given file with its logical equivalent the object is to derive simple files with no redundant elements.
Operation System: In database – machine based software that facilitates the availability of information or reports through the DBMS.
Password: Identity authenticators a key that allow access to a program system a procedure.
Record: A collection of aggregates or related items of a data treated as a unit.
Source Code: A procedure or format that allow enhancements on a software package.
System: A regular or orderly arrangements of components or parts in a connected and interrelated series or whole a group of components necessary to some operation.
System Design: Detailed concentration on the technical and other specification that will make the new system operational.
1.8 ORGANIZATION OF THE WORK
The project is organized in five chapters. With introduction already being explained in chapter 1 and the whole idea of this research work presentation in chapter one, like objective of the study, statement of the research area of coverage limitation and definition of terms all this makes up the chapter one.
Chapter 2; this section deals with the review of study, review of concept theories upon which this work is built on, the potential issues in the any face recognition system in the form of difference in the lighting conditions in which the same picture appears differently and the variations in skin color and pose.
Chapter 3 talks about the software tools used in the project mainly related to visual basic programming language. The methodology at which this research work will be implemented.
In chapter 4 the system is implemented and presented with its analysis. Functions of the system and the operation of the system is also, in depth explained for reader understating and comprehension.The system requirement is also detailed and the platform at which the system can run on.
Chapter 5 summaries the whole work done and make possible recommendation and suggest other points to be included into the work for future propose
REFERENCE
- N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, 1997, pp. 711-720.
- H. Press, S. A. Teukolsky and W. T. Vetterling, Numerical recipes in C: the art of scientific computing, 2nd ed., Cambridge University Press, 1992. ISBN:0-521-43108-5
- Sajid, M.M. Ahmed, I. Taj, M. Humayun, F. Hameed, “Design of High Performance FPGA based Face Recognition System”, PIERS 2008 in Cambridge, USA, 2-6 July, 2008.
- Zhang, h. Chang, ” A literature survey of face recognition and reconstruction technique”, Technical report, University of Texas, December, 2005.
- Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol. 3, no. 1. 1991, pp. 71-86. [CrossRef]
Matsumoto T., Matsumoto H., Yamada K., Hoshino S., Impact of artificial “gummy” fingers on fingerprint systems, in Optical Security and Counterfeit Deterrence Techniques IV, vol. 4677 of Proceedings of SPIE, pp. 275–289, San Jose, Calif, USA, January 2002.
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DESIGN AND IMPLEMENTATION OF FACE DETECTION AND RECOGNITION SYSTEM
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