Security and authentication of a person is a vital part of any business. There are many techniques use d for this purpose. One of technique is human face recognition . Human Face recognition is an effective means of authenticating a person. The benefit of this approa ch is that,it enables us to detect changes in the face pattern of an individual to substantial extent. The recognition s ystem can tolerate local variations in the face exp ression of an individual. Hence Human face recognition can be use d as a key factor in crime detection mainly to iden tify criminals. There are several approaches to Human fa ce recognition of which Image Processing Principal Component Analysis (PCA) and Neural Networks have been includ ed in our project. The system consists of a databas e of a set of facial patterns for each individual. The charact eristic features called �eigenfaces� are extracted from the stored images using which the system is trained for subseq uent recognition of new images.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
I am Adoitya Kaila .a student of management.here I am presnting a presentation on biometric technology which is considered the most reliable source of security in todays time.i have tried to make it simple for each and everyone .
Star Link Communication Pvt. Ltd., India's leading manufacturer of biometric attendance system and access control system, brings you this slideshow about biometrics and how the technology works.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
I am Adoitya Kaila .a student of management.here I am presnting a presentation on biometric technology which is considered the most reliable source of security in todays time.i have tried to make it simple for each and everyone .
Star Link Communication Pvt. Ltd., India's leading manufacturer of biometric attendance system and access control system, brings you this slideshow about biometrics and how the technology works.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Comparative Studies for the Human Facial Expressions Recognition Techniquesijtsrd
This article reviews the different techniques for recognizing facial expressions. First, it gives a description of the emotions their types and the techniques to measure the emotions. Then it talks about the identification of the face and then the techniques for extracting the features from the face. Then the various classifiers designed to classify these extracted features are discussed. Finally, a comparative study of some of the recent studies has been presented. Sheena Gaur | Mayank Dixit | Sayed Nasir Hasan | Anwaar Wani | Tanveer Kazi | Ahsan Z Rizvi "Comparative Studies for the Human Facial Expressions Recognition Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28027.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/28027/comparative-studies-for-the-human-facial-expressions-recognition-techniques/sheena-gaur
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Magnetic abrasive finishing is a machining process where the tooling allowance is remove by media wi th both magnetic and abrasive properties,with a magnetic f ield acting as a binder of a grain. Such machining falls into the category of erosion by abrasive suspension and lend itself to the finishing of any type of surface . The possibility of finishing complex surfaces is a spec ial benefit of this machining. Magnetic abrasive fi nishing process is most suitable for obtaining quality fini sh on metallic and non-metallic surfaces. Magnetic abrasive finishing used for complicated product finishing & Roughness and tolerance band achieved that is diffi cult using conventional machine process. The product dimension al requirement easily possible with taking trial wi th MAF parameters.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORK
1. Novateur Publication’s
International Journal of Innovation in Engineering, Research and Technology [IJIERT]
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HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND
NEURAL NETWORK
Yogesh B. Sanap
PG Student, Department of Computer Science & Engineering,
TPCT’s College of Engineering,
Osmanabad, Maharashtra, India
Dr. Anilkumar N. Holambe
HOD & P.G. Coordinator, Department of Computer Science & Engineering,
TPCT’s College of Engineering,
Osmanabad, Maharashtra, India
ABSTRACT
Security and authentication of a person is a vital part of any business. There are many techniques used for this
purpose. One of technique is human face recognition. Human Face recognition is an effective means of
authenticating a person. The benefit of this approach is that, it enables us to detect changes in the face pattern of an
individual to substantial extent. The recognition system can tolerate local variations in the face expression of an
individual. Hence Human face recognition can be used as a key factor in crime detection mainly to identify
criminals. There are several approaches to Human face recognition of which Image Processing Principal Component
Analysis (PCA) and Neural Networks have been included in our project. The system consists of a database of a set
of facial patterns for each individual. The characteristic features called ‘eigenfaces’ are extracted from the stored
images using which the system is trained for subsequent recognition of new images.
MANIFESTATION TERM – Biometrics, Neural Networks (NN), Principal Component Analysis (PCA), Eigen
Values, Eigen Vector, Image Processing.
INTRODUCTION
FACE RECOGNITION SYSTEM
A face recognition system is a computer application for automatically identifying or verifying a person from a
digital image or a video frame. One of the ways to do this is by comparing selected facial features from the image
and a facial database. Security and authentication of a person is a crucial part of any industry. There are many
techniques used for security and authentication one of them is face recognition. Face recognition is an effective
means of authenticating a person the advantage of this approach is that, it enables us to detect changes in the face
pattern of an individual to an appreciable extent the recognition system can tolerate local variations in the face
expressions of an individual. Hence facial recognition can be used as a key factor in crime identification and
detection, mainly to identify criminals there are several approaches to facial recognition of which Image processing
principal component analysis (PCA) and neural networks (NN) have been incorporated in our project face
recognition as many applicable areas. Moreover it can be categories into face recognition, face classification, one, or
sex determination. The system consists of a database of a set of facial patterns for each individual. The characteristic
features called ‘Eigen faces’ are extracted from the storage images using which the system is trained for subsequent
recognition of new images.
It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris
recognition systems
BASIC OF FACE RECOGNITION
The first step in Human face recognition system is to detect the Human face in an image. The main objective of
human face detection is to find whether there are any human faces in the image or not. If the Human face is present,
then it returns the location and position of the image and extent of the each Human face. Pre-processing is done to
remove the noise and reliance on the precise registration. The block diagram of a typical face recognition system can
be shown with the help of Figure. The face detection and Human face extraction are carried out simultaneously. The
complete process of face recognition can be shown in the Figure 1.
2. Novateur Publication’s
International Journal of Innovation in Engineering, Research and Technology [IJIERT]
ICITDCEME’15 Conference Proceedings
ISSN No - 2394-3696
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Figure 1 Block diagram of a Face Recognition
There are various factors that makes the human face detection is a challenging task. Pose presence or absence of
structural mechanism, Facial look and expression, Occlusion, Image direction. The facial feature detection is the
process to detect the presence and location of features like eyebrow, nose, eyes, lips, nostrils, mouth, ears, etc. this is
done with the assumptions that there is only a single human face in an image. In the Face recognition process the
input image is compared with the stored database. The input image is also called as probe and the database is called
as gallery. Then it gives a matching report and then the classification is done to identify the sub-population to which
new observations belong
LITERATURE REVIEW
TECHNIQUES USED FOR FACE RECOGNITION
a. TRADITIONAL
There some facial recognition algorithms identify faces by extracting important features from an image of
the subject's human face. For example, an algorithm may analyze the relative position, size, shape of the
eyes, eyebrows, nose, cheekbones, and jaw. These features are then used to search for other images with
matching features from stored database. Other algorithms normalize a gallery of face images and then
compress the face data, only saving the data in the image that is useful for human face detection. A probe
image is then compared with the face data. Popular recognition algorithms include Eigen-face, fisherface,
the Hidden Markov model, and the neuronal motivated dynamic link matching.
b. 3D
A newly trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition.
This technique uses 3D sensors to gather information about the shape of a face. This information is then
used to identify unique features on the surface of a face, such as the contour of the eye sockets, nose, and
chin. One advantage of 3D human face recognition is that it is not affected by changes in lighting like other
techniques. It can also identify a human face from a range of viewing angles, including a profile view.
c. SKIN TEXTURE ANALYSIS
It is another trend which uses the visual details of the skin, as captured in standard digital or computer
scanned images. This technique, called skin texture analysis, turns the unique lines, patterns, and spots
apparent in a person’s skin into mathematical calculations. Tests have shown that with the addition of skin
texture analysis, performance in recognizing human faces can increase 20 to 25 percent.
PROBLEM STATEMENT
In face recognition system, the face detection and feature mapping is core concern to analyze the face. There is need of training
for detection of faces from different perspective. The efficiency is the main concern for feature selection and multiple algorithm
proposed for solve the accuracy problems. In the past techniques, there is need to provide the large test or training images to
detect and assign the particular class means efficient image processing. Automatic recognition of human face is a challenging
problem which has received much attention during recent years due to its many applications in different fields. Human Face
recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all
situations.
EXISTING SYSTEM
• Human Face recognition is the computer application technique to recognize a human face. When a person is
registered in a face recognition system, a video camera takes a series of snapshots of the human face and then
represents it by a unique code
• When person has their face verified by the computer system, it captures their current appearance look and compares it
with the facial unique codes already stored in the database system.
3. Novateur Publication’s
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• When human faces match, the person receives authorization for the system; otherwise, the person will not be
authorized. The existing human face recognition system identifies only static face images that almost exactly match
with one of the images stored in the image database.
• When the current image captured almost exactly matches with one of the images which is stored in database then
only the person is authorized and granted access.
• When the current image of a person is considerably different, after comparing with image database say, in terms of
facial expression then person will be denied
PROPOSED SYSTEM
The proposed human face recognition system overcomes certain pitfalls of the existing human face recognition system. It is
based on extracting the important features of a set of human faces stored in the database and performing mathematical
operations for the comparison on the values corresponding to them. Hence when a new image is fed into the system for
recognition the important features are extracted and compute result to find the distance between the input image with the stored
database images. Proposed system can tolerate some variation in the new face image. When the new image of a person varies
from the images of that person stored in the database, the system will be able to recognize the new face and identify person. The
proposed system is better mainly due to the use of facial features rather than the entire face. Advantages in terms of
• Human Face recognition accuracy and better unfair power Computational cost because smaller images require less
processing to train the image processing PCA.
• The use of dominant features and hence can be used as an effective means of authentication
IMPLEMENTATION
FACE RECOGNITION USING NEURAL NETWORK
We classify our project into two parts. These two stages are:-
1) Feature Extraction (Training)
2) Classification (Testing)
The first part of our project is feature extraction (Training). Feature Extraction is done with the help of image
processing PCA (Principle component Analysis) Technique and Classification will done with the help of Neural
Network. Here we considered the Yale Database, in which the database is having the images of 15 persons with 11
different positions. Every image in the database is of size 240 X 300 pixels means each image is having 72000
pixels and the number of images are 165. So the total pixels processing is 72000 x 165. The processing of such a
huge data is difficult and also time consuming. So we required to minimize the data first without interfering the
image features and contents. So to reduce the number of pixels processed per image we use here the concept of
image down sampling.
Training Faces
Step 1: Prepare the data
In this step, the faces constituting the training set (Γ) should be prepared for processing. Firstly we down sampled
these images to 60 X 80 pixels i.e. the down sampling ratio is 0.25. So the number of pixels is reduced from 72000
to 4800 pixels. Due to that, the speed of operation of our project has increased.
Now from those images, we considered either all eleven images for training or depends on our choice, although we
can say that eight images are used for training but it may be possible that subject came in front of system may
having passion from that remaining three images. As the number of images for training is increased the result has
also increased.
Down Sampling = |Xn / N|
Where, Xn- Image Pixel Values and N- Down Sampling Rate. Here we considered N=4. Figure bellow shows the
Down sampling image.
Figure 2 down sampling of Image
Image with pixel
value of 240 X 320
Down Sampling Image with pixel value
of 60 X 80
4. Novateur Publication’s
International Journal of Innovation in Engineering, Research and Technology [IJIERT]
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The Example of down sampled image is shown in figure bellow. The input image is of size 240 X 300 pixels and the
down sampled image is of size 60 x 80 pixels.
Figure 3 Input images with 240 x 300 pixels Down sampled image with 60 x 80 pixels
• We has arranged all the images in the in the row wise with each row size of 60 X 80 = 4800 pixels per row
Step 2: find the mean
The average matrix Ψ has to be calculated, then subtracted from the original faces (Γ) and the result stored in the
variable ɸi
Ψ = M ∑n-1M Γ
ɸ = Γ – Ψ
Step 3: find the covariance matrix, Covariance C is calculated
C=
• We formed a matrix of size 4800 X 165 pixels. From that we had calculated variance and covariance. After
covariance the new matrix is formed of size 4800 X 4800 pixels
• The formula for variance and covariance is shown bellow
----- Variance
-------- Covariance
Step 4: find the eigenvectors, Eigen-values of the covariance matrix in below step
Description of the correct algorithm for determination of eigenvectors and Eigen-values is absent here, as it belongs
to the standard arsenal of most math programming libraries.
• After that, we had calculated Eigen Values of matrix size 4800 X 4800 and Eigen vector of size 4800 X 1.
5. Novateur Publication’s
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ISSN No - 2394-3696
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Figure 4 Rough sketches for Eigen value and vector calculation
• From these Eigen values and Eigen vector we drawn the Eigen face. The Eigen faces we calculate are 20
instead of 15 (as we use 15 persons) for our convenience and for result improvement
Step 5: find the principal components
M eigenvectors (Eigen-faces) ui, only M' should be chosen, which have the greatest Eigen-values. The greater the
Eigen-value, the more characteristic features of a human face does the particular eigenvector describe. Eigen-faces
with low Eigen-values can be retrieved, as they explain only a small part of characteristic features of the faces.
After M' Eigen-faces ui are determined, the “training” phase of the algorithm is finished.
• We had calculated the 20 PCA values as we consider here 20 Eigenface. So for each Eigenface the PCA is
being calculated. The size of that calculated PCA is 4800 X 20 for all images. Now, PCA value is 240 X
20 matrixes for every person i.e. for 11 posses of a person. PCA is well known statistical procedure that
can be used to derive important features by reducing the dimensions of given input vector without losing
information. The features of interest may not be available explicitly, and must be derived from the available
attributes of individuals.
The feature extraction step is completed after image processing PCA values are being calculated. Once PCA
values are being calculated, the same PCA values are used for testing purpose to avoid the overhead of the project
and increase the speed of our system.
RESULT
FACE RECOGNITION RESULT
The input image was given to program for recognition. The input images have different positions. For this, whether
the input image with different posses was matched or not was shown in figures bellow.
Figure 5 Wink face position
Training image of size
4800 pixels
Image Matrix of size
4800 X 4800 pixels
After Covariance
calculation
Eigen Vector of size
4800X4800
Eigen Value of
size 4800X1
6. Novateur Publication’s
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Figure 6 Left light position
Figure 7 Surprised Face Position
Figure 8 Wear Glass position
Figure 9 Left light possessions