CHAPTER 1. INTRODUCTION
1.1 PROJECT OVERVIEW
A Face Recognition System is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. This system prefers the ways to do this is by comparing selected facial features from the image and a facial database. It would be typically used in security systems and can be compared to other biometrics such as fingerprint or iris recognition systems.
It follows the following steps:
Actions everyday are increasingly being handled electronically, instead of pencil and paper.
This growth in electronic industries results in great request for fast and accurate user identification and authentication.
Access codes for buildings, bank accounts and computer systems often use PIN’s identification and security clearances.
We may already be engaging with facial recognition technology on a day-to-day basis without realizing, but there are massive scope for the future of tech development.
Its aim, is to reach, and develop a method of face recognition that is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and techniques.
It has a wide scope in personal, commercial and national security.
The objective of Face Recognition is to identify or verify a face from a database where similar faces exists. It is done by comparing selected facial features from the image and a facial database.
Some of the objectives of using Face Recognition are:
It requires no physical interaction on behalf of the user
It is accurate and allows for high enrolment and verification rates.
It can use existing hardware infrastructure, existing cameras and image capture devices will work with no problems.
It finds a face within a large database of faces.
The face is our primary focus of attention in social life playing an important role in conveying identity and emotions.
CHAPTER 2. SYSTEM ANALYSIS
2.1 USER CHARACTERISTICS
As Face Recognition is a technique which deals with automatically identifying or verifying a person from a video frame or a video source and so this software can be used by anyone. Though the central users of this software are:-
TOOLS AND TECHNOLOGY
2.2.1 Hardware (Minimum):
Microprocessor: Intel core i5
RAM: 2 GB
Hard disk: 50 GB
CHAPTER 3. SYSTEM DESIGN
3.1 FLOW OF SYSTEM
The figure below depicts the complete flow of the system with an overview of all the modules included in it.
Input Data for Database
Add Data to Database in SQLite (Id, Name, Age, Gender)
Add Capture Images to Image Dataset
Image Capture for Dataset
Training the Image Dataset
Classifying Face Database in SQLite
Comparing detected Face with Face Database in SQLite
Fig: 3.3.1 Flow of System
3.2 Major Functionalities
Accessing devicesWe can unlock our phones with our fingerprints, so today essentially all phones, laptops and tablets are fitted with cameras. Say, for instance, your phone was stolen. Only you would be able to unlock it, making it worthless to anyone who isn’t an experienced hacker. Once proven accurate, this could encourage consumers to store more personal information on devices
Unlocking carsIf one day, we can access our personal electronic devices with our physical appearances, then this could also be the case for other connected possessions. One of the most obvious examples are cars. Some cars has already been working on walking gait recognition software, and cars can already recognise and respond to surrounding environments. Eventually, this will probably include recognising their owners.
Targeted advertisingUsing a screen kitted out with some software, the grocery giant aimed to offer more relevant advertising to the benefit of company and consumer. Today, other retail companies are looking to install similar software that identifies customers as they enter shops, changing display boards to suit their personal preferences.
Marketing responseAs well as discovering the best ways to connect with potential and existing customers, facial recognition technology is also being used to judge levels of engagement. Some stores are rumoured to be developing its own face recognition system to gain insights into customer satisfaction. Eventually, this could become standard procedure for all major retailers.
Securing dataUsing biometric authentication, sensitive digital data could be secured from malicious influences. Data security has become a pressing issue, and not just for the boardroom. Now, it’s important for everyone to know exactly what information they have, and how to make sure it’s safely stored
Health diagnosisThe facial recognition tech can be used to detect diseases, but what about the healthcare issues that aren’t so easy to spot. Through carefully tracking a patient’s expressions, medical staff could judge the extent of distress and come closer to making an accurate diagnosis. This, of course, has uses outside of healthcare facilities too.
Assisting the blindSome of the firm has created a mobile app that enables the blind or visually impaired to know when someone is smiling at them. This new way to experience the world could help blind people to forge more meaningful connections with others, easing the isolation that can come with a sensory defect.
CHAPTER 4. IMPLEMENTATION
4.1 IMPLEMENTATION ENVIRONMENT
Python is an open source programming language. Python was made to be easy-to-read and powerful. A Dutch programmer named Guido van Rossum made Python in 1991.
Python is an interpreted language. Interpreted languages do not need to be compiled to run. A program called an interpreter runs Python code on almost any kind of computer. This means that a programmer can change the code and quickly see the results. This also means Python is slower than a compiled language like C, because it is not running machine code directly.
Python is a good programming language for beginners. It is a high-level language and can focus on what to do instead of how to do it. Writing programs in Python takes less time than in some other languages.
Python drew inspiration from other programming languages like C, C++, Java, Perl, and Lisp.
Python is used by hundreds of thousands of programmers and is used in many places. Sometimes only Python code is used for a program, but most of the time it is used to do simple jobs while another programming language is used to do more complicated tasks.
Its standard library is made up of many functions that come with Python when it is installed. These libraries make it a powerful language.
Some things that Python is often used for are:
OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source BSD license.
The goals of OpenCV are:
Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel.
Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable.
Advance vision-based commercial applications by making portable, performance-optimized code available for free – with a license that did not require code to be open or free itself.
SQLite is a relational database management system contained in a C programming library. In contrast to many other database management systems, SQLite is not a client–server database engine. Rather, it is embedded into the end program.
SQLite is ACID-compliant and implements most of the SQL standard, using a dynamically and weakly typed SQL syntax that does not guarantee the domain integrity.
SQLite is a popular choice as embedded database software for local/client storage in application software such as web browsers. It is arguably the most widely deployed database engine, as it is used today by several widespread browsers, operating systems, and embedded systems (such as mobile phones), among others. SQLite has bindings to many programming languages.
4.3 CODING STANDARDS
Coding standards for Python
Four Space Indentation
Parentheses Round Expressions
Multiple Statements per Line
4.4 SNAPSHOTS OF PROJECT
Fig: 4.4.1 Dataset Generator
Fig:4.4.2 Dataset Generated
Fig:4.4.3 Dataset Trainer
Fig:4.4.4 SQLite Database Structure
Fig:4.4.5 SQLite Database Data
Fig:4.4.6 Face Recognized
CHAPTER 5. CONSTRAINTS AND FUTURE ENHANCEMENTS
Face recognition is not perfect and struggles to perform under certain conditions.
Developing a computational model of face detection and recognition is quite difficult because faces are complex, multidimensional and meaningful visual stimuli.
Face recognition does not work well include poor lighting, sunglasses, long hair, or other objects partially covering face and low resolution images.
Some systems are less effective if facial expressions vary.
Difficulties with data processing and storing
A possible future application for facial recognition systems lies in retailing. A retail store may have cash registers equipped with cameras; the cameras would be aimed at the faces of customers, so pictures of customers could be obtained. The camera would be the primary means of identifying the customer, and if visual identification failed, the customer could complete the purchase by using a PIN. After the cash register had calculated the total sale, the face recognition system would verify the identity of the customer and the total amount of the sale would be deducted from the customer’s bank account. Hence, face-based retailing would provide convenience for retail customers, since they could go shopping simply by showing their faces, and there would be no need to bring debit cards, or other financial media. Wide-reaching applications of face-based retailing are possible, including retail stores, restaurants, movie theatres, car rental companies, hotels, etc.
CHAPTER 6. CONCLUSION
Face recognition systems have been related generally with very expensive top secure applications. Today the essential technologies have advanced and the cost of equipment is going down intensely due to the combination and the increasing processing power. Certain applications of face recognition system are now cost effective, reliable and highly accurate. And so there are no technological or financial obstacles for stepping from widespread deployment. Though there are some weaknesses of facial recognition system, there is a tremendous scope in India. This system can be effectively used in ATM’s, recognizing duplicate voters, passport and visa verification, driving license verification, exams and government and private sectors. Face Recognition is both a challenging and important recognition technique. Among all the biometric techniques, face recognition approach possesses one great advantage, which is its user-friendliness.
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