AutoViRe: Automated Visage Recognition Attendance System

A Project Work(A80088)

Submitted

in partial fulfillment of the requirements for

the award of the degree of

Bachelor of Technology

in

Computer Science and Engineering

By

Ms. ANCHAL AGRAWAL (15261A05C3)

Mr. MOHAMMED WASEF MOHIUDDIN (15261A05F4)

Under the Guidance of

Mrs. B. Prasanthi

(Assistant Professor)

and

Mrs. M. Mamatha

(Assistant Professor)

Department of Computer Science and Engineering

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

(Affiliated to Jawaharlal Nehru Technological University)

GANDIPET, HYDERABAD – 500 075. TELANGANA (INDIA)

APRIL 2019

i

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

(Affiliated to Jawaharlal Nehru Technological University Hyderabad)

GANDIPET, HYDERABAD – 500 075. Telangana

CERTIFICATE

This is to certify that the project entitled “AutoViRe: Automated Visage Recognition

Attendance System” is being submitted by ANCHAL AGRAWAL bearing Roll No:

15261A05C3 and MOHAMMED WASEF MOHIUDDIN bearing Roll No: 15261A05F4 in

partial fulfillment for the award of B Tech in Computer Science and Engineering to Jawaharlal

Nehru Technological University, Hyderabad is a record of bonafide work carried out by

him/her under our guidance and supervision.

The results embodied in this project have not been submitted to any other University or

Institute for the award of any degree or diploma

Supervisor Supervisor Head of the Department

Mrs. B. Prasanthi Mrs. M. Mamatha Dr. C.R.K. Reddy

Asst. Professor Asst. Professor Professor

External Examiner

ii

DECLARATION

This is to certify that the work reported in this project titled “AutoViRe: Automated Visage

Recognition Attendance System” is a record of work done by us in the Department of

Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad.

No part of the work is copied from books/journals/internet and wherever the portion is

taken, the same has been duly referred in the text. The report is based on the work done entirely

by us and not copied from any other source

ANCHAL AGRAWAL (15261A05C3)

MOHAMMED WASEF MOHIUDDIN (15261A05F4)

iii

ACKNOWLEDGEMENT

We would like to express our sincere thanks to Dr. K Jaya Sankar, Principal MGIT, for

providing the working facilities in college.

We wish to express our sincere thanks and gratitude to Dr. C R K Reddy, Professor and

HOD, Department of CSE, MGIT, for all the timely support and valuable suggestions during

the period of project.

We are extremely thankful to Dr. M Rama Bai, Professor, Dr. K Sreekala, Assistant

Professor, Mrs. J Sreedevi, Assistant Professor, Department of CSE, MGIT, Major Project

Coordinators for their encouragement and support throughout the project.

We are extremely thankful and indebted to our internal guides Mrs. B. Prasanthi, Assistant

Professor and Mrs. M. Mamatha, Assistant Professor, Department of CSE, for their constant

guidance, encouragement and moral support throughout the project.

Finally, we would also like to thank all the faculty and staff of CSE Department who helped us

directly or indirectly, for completing this project.

ANCHAL AGRAWAL (15261A05C3)

MOHAMMED WASEF MOHIUDDIN (15261A05F4)

iv

TABLE OF CONTENTS

Certificate i

Declaration ii

Acknowledgement iii

List of Figures vi

List of Tables viii

Abstract ix

1. Introduction 1

1.1 Existing Attendance Systems 3

1.1.1 Basic Attendance Systems 3

1.1.2 Moderate Attendance Systems 4

1.1.3 Advanced Attendance Systems 5

1.1.4 Drawbacks of Existing Systems 5

1.2 Proposed Attendance System 7

1.3 Requirements Specifications 9

1.3.1 Software Requirements 9

1.3.2 Hardware Requirements 9

1.3.3 Developer Requirements 9

2. Literature Survey 10

2.1 Previously Implemented Techniques for Attendance Systems 10

2.2 Tabular Representation 12

3. AutoViRe: Automated Visage Recognition Attendance System 13

3.1 Modules Description 13

3.1.1 Feature Registration 13

3.1.2 Facial Recognition 15

3.1.3 Attendance Update 15

3.1.4 Email Notification 16

3.2 Design Methodology 17

3.2.1 Activity Diagram 17

3.2.2 State Chart Diagram 19

3.2.3 Class Diagram 21

3.2.4 Sequence Diagram 23

v

3.2.5 Collaboration Diagram 24

3.2.6 Use Case Diagram 25

4. Testing including Test Cases and Results 26

4.1 Test Cases 26

4.2 Results 27

5. Conclusions and Future Scope 31

5.1 Conclusion 31

5.2 Future Scope 31

Bibliography 32

Appendix A – Source Code 33

Appendix B – User Manual 52

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LIST OF FIGURES

Figure 3.1 Feature Registration 13

Figure 3.2 Facial Recognition 14

Figure 3.3 Attendance Update 15

Figure 3.4 Facial Registration and Facial Recognition 16

Figure 3.5 Activity Diagram – Decision Making and Implementation 17

Figure 3.6 State Chart Diagram – Representation of States and their transformation 19

Figure 3.7 Class Diagram – Representation of classes and their relationships 21

Figure 3.8 Sequence Diagram – Object Interaction and Process Execution 23

Figure 3.9 Collaboration Diagram – Object Interaction and Process Execution 24

Figure 3.10 Use Case Diagram – Application’s Functionality based on Input Type 25

Figure 4.2 Feature Registration Screenshot 26

Figure 4.3 Facial Recognition Screenshot 28

Figure 4.4 Digital Attendance Sheet 29

Figure 4.5 Email Notification 30

Figure B.1 Creation of Django Project 52

Figure B.2 Databases Class 53

Figure B.3 Time_Zone Class 53

Figure B.4 Making database migrations into the project 53

Figure B.5 Running the localhost server 54

Figure B.6 Creation of Django app 54

Figure B.7 Installed_Apps class 54

Figure B.8 Making migrations into Django app 55

Figure B.9 Creation of Super User for Admin 55

Figure B.10 Running localhost server 55

Figure B.11 Admin page login for the Django localhost 56

Figure B.12 Admin project Admin Page 56

Figure B.13 Redirecting to Project Path 57

Figure B.14 Running server on command line 58

Figure B.15 Browser Display of Application Execution 59

Figure B.16 Registration based on Unique Student ID 60

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Figure B.17 Facial Registration 61

Figure B.18 Dataset consisting of several registered faces 62

Figure B.19 Browser Display of training webpage 63

Figure B.20 Training display on the command line 63

Figure B.21 Completion of training 63

Figure B.22 Browser display of final testing 64

Figure B.23 Successful Face Detection based on dataset 65

Figure B.24 Final tally of attendance for the current day 66

Figure B.25 Final updated database 67

Figure B.26 Email sent to the respective parent 68

viii

LIST OF TABLES

Table 2.1 Tabular Representation of the Background Research 12

Table 4.1 Test Cases and Outputs 26

ix

ABSTRACT

In educational institutions, companies and various organizations, one of the main criteria is to

maintain a record of people present daily. Attendance Management hence becomes an

important goal for the progress and integrity of any organization. The system revolves around

the concept of tallying registered values in the database with the current inputs. Each input is

compared to existing values in database and if the value exists, the given candidate is marked

present. Otherwise, the candidate is marked absent.

Hardware-based attendance systems that store values in a database currently exist and are the

most common record tracking systems found in organizations. There are some automatic

attendances making system which are currently used by much institution. One of such system

is biometric technique. Although it is automatic and a step ahead of traditional method it fails

to meet the time constraint. The student has to wait in queue for giving attendance, which is

time taking.

The proposed system deals with automating the attendance recording procedure in an efficient

and optimized manner. It follows a tallying procedure to record attendance and stores it in the

system. This project introduces an involuntary attendance marking system, devoid of any kind

of interference with the normal teaching procedure.

The system can be also implemented during exam sessions or in other teaching activities where

attendance is highly essential. This system eliminates classical student identification such as

calling name of the student, or checking respective identification cards of the student, which

can not only interfere with the ongoing teaching process, but also can be stressful for students

during examination sessions.

In organizations & educational institutions, attendance updating takes place continuously and

these results in dynamic changes. The system would provide the administrator the access to

attendance tracking dynamically & maintain it efficiently.

1

1. Introduction

Uniqueness or individuality of an individual is his face and its features. In this project, face of an

individual is used for the purpose of marking attendance automatically. Conventional methodology

for taking attendance is by calling the name or roll number of the student and the attendance is

manually recorded. The important point of concern with this methodology is Time Consumption.

On an average it takes an essential part of the time allotted for the subject.

Attendance is prime important for both the teacher and student of an educational organization. So,

it is very important to keep record of the attendance. The problem arises when we think about the

traditional process of taking attendance in class room. Calling out the name or roll number of the

student for attendance is not only a problem of time consumption but also consumes energy. So,

an automated attendance system can resolve all the above problems.

To stay away from these losses, an automated process has been used in this project which is based

on Image Processing. In this project, the two main components that have been used are face

detection and face recognition.

Face detection is used to locate the position of the face region and face recognition is used for

marking the understudy’s attendance. The database of all the students in the class is stored and

when the face of the individual student matches with one of the faces stored in the database then

the attendance is recorded.

There exists a wide range of automatic attendance management systems which are currently being

used by many institutions and organizations. One such system is the biometric based attendance

system. Although it is automatic and leaps a step ahead of the traditional method it fails to meet

the time constraint because the students have to wait in a queue for marking attendance, which is

also a time taking process.

2

This project introduces an involuntary attendance marking system, devoid of any kind of

interference with the normal teaching procedure. The system can be also implemented during exam

sessions or in other teaching activities where attendance is highly essential.

This system eliminates classical student identification such as calling names of the students, or

checking respective identification cards of the students, which can not only interfere with the

ongoing teaching process, but also can be stressful for students during examination.

The proposed system deals with automating the attendance recording procedure in an efficient and

optimized manner. Our proposed system shall be a Face Recognition Attendance System which

uses the basic idea of image processing which is used in many secure applications like banks,

airports etc. It follows a tallying procedure to record attendance and stores it in the system.

Section 1.1 specifies the Requirements for the Development of the Application.

Section 2 gives the comparison and descriptions of the Existing Attendance Systems and Proposed

Attendance Systems along with its uses.

Section 3 describes the Overview of the modules and the Design of the overall application.

Section 4 mentions all the Test Cases and their outcomes along with the Execution Results of the

application.

Section 5 throws light on the Final Conclusions and Future Scope that we can derive from the

application and its uses.

3

1.1 Existing Attendance Systems

Attendance is prime important for both the teacher and student of an educational organization. So,

it is very important to keep record of the attendance. There are various attendance management

systems that vary in complexity and feasibility. We have divided them into three categories

namely, basic, moderate, and advanced.

1.1.1. Basic Attendance Systems

a. Manual Attendance System:

The Manual Attendance System involves the process of the faculty calling out the roll calls. If the

student is present in the class, the student physically acknowledges the roll call and says that he/she

is present. In all other cases, the faculty marks the student absent.

b. Paper Based Attendance System:

The Paper Based Attendance System is a part of the manual attendance system or could be used

for any other attendance system as well. Attendance is taken in any form and it's recorded on a

paper by writing either the absentees, or the presentees only. Usually faculties write the roll

numbers of the students that are absent or present as per convenience.

c. Timesheet Attendance System:

The Timesheet Attendance System involves recording attendance into a timesheet. A timesheet is

a physical or virtual tool that allows you to record and keep track of your worked time, in this case

it's number hours the student attends.

d. Token Based Attendance:

The Token based attendance involves displaying of a security token when demanded in order to

secure attendance. A security token (sometimes called an authentication token) is a small hardware

device that the owner carries to authorize access to a network service. The device may be in the

form of a smart card or may be embedded in a commonly used object such as a key fob. In the

context of students, the token is usually their identity card.

4

1.1.2 Moderate Attendance Systems

a. Biometric Attendance System:

The biometric attendance system works on two basic principles. First, it takes an image of a finger.

Then finger scanner saves characteristics of every unique finger and saved in the form of biometric

key. Actually, finger print scanner never saves images of a finger only series of binary code for

verification purpose. Secondly, the biometric attendance system determines whether the pattern of

ridges and valleys in this image matches the pattern of ridges and valleys in pre-scanned images.

b. Badge Monitoring Attendance System:

The Badge Monitoring Attendance System[3] is most commonly used in places where people work

with radioactive materials such as in a X-ray lab, nuclear centres, etc. The radioactive badge is

worn by the person somewhere between the neck and the waist such that the front faces the source

of radiation.

c. Swipe Card Attendance:

The Swipe Card Attendance System works by the person swiping its card on entry and exit of the

gate, and the attendance is recorded. A swipe card must come in contact with the corresponding

card reader before any transaction can take place. The transaction becomes active when the

magnetic stripe on a card is moved through a console at a gate.

d. Access Card Punching Attendance System:

A punch card is a flat and stiff paper with notches cut in it and contains digital information. In

punch card attendance system, students use this punch or proximity card for in and/or out. To

use a punch card, students just need to wave the card near a reader, which then ensures whether

the correct person is logging in and/or out.

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1.1.3 Advanced Attendance Systems

a. Retinal Scan-based Attendance System:

The Retinal Scan-based Attendance System makes uses of retinal features and marks attendance

on retinal recognition. Eye scan or retinal scan is a biometric system that identifies a person by

using unique patterns of the retina. Human retina contains a complex blood vessel (retinal vein)

patterns through which an eye scanner device can easily identify a person and can even

differentiate identical twins. To scan human retina, retinal scanner uses the reflection of light that

is absorbed by retinal vein.

b. Gait Recognition Attendance System:

The Gait Recognition Attendance System records and recognizes an individual by examining the

way an individual walks, saunters, swaggers, or sashays — with up to 90-percent accuracy.

c. Facial Recognition Attendance System:

The Facial Recognition Attendance System[5] makes use of facial features such as distance

between the eyes, width of the nose, depth of the eye sockets, the shape of the cheekbones, the

length of the jaw line, etc. to recognize and mark attendance.

d. Sensor Detection Attendance System:

The Sensor Detection Attendance System uses RFID (Radio Frequency Identification)[4] to

identify individuals. A radio frequency identification reader (RFID reader) is a device used to

gather information from an RFID tag, which is used to track individual objects. Radio waves are

used to transfer data from the tag to a reader. RFID is a technology similar in theory to bar codes.

1.1.4 Drawbacks of Existing Systems:

a. Accuracy:

With an automated system, there is no human error. When you manually track your

students’ time, your students typically report their hours after they've worked them. This

will often increase the likelihood of inaccurate reporting. A student may not intend to

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misrepresent his hours, he may just forget what his actual in and out times were. Or, if a

student has illegible handwriting, it could make it difficult for roll list to determine actual

hours attended. With manual reporting, the organization is basically relying on the honor

system. This system can be abused, which can lead to time theft.

b. Increased Productivity:

Organizations who use a manual roll call process spend several hours each day collecting

time cards, re-entering an illegible data by hand, faxing, phoning, and processing rollcall.

When you employ an automated time and attendance system, the rollcall process takes just

minutes each period.

c. Savings:

With an automated system, you’ll save rollcall processing hours and eliminate time theft

which means your bottom line will improve.

d. Regulatory Compliance:

An automated time and attendance system will not guarantee you’ll be compliant with all

student laws, the data that’s collected through the system can ensure you have the

information at your fingertips you’ll need to comply with all labour regulations. With an

automated timekeeping system, you’ll have the ability to pull up reports quickly. This will

provide you with all the information you’ll need if you’re ever subject to an audit.

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1.2. Proposed System

The proposed system “AutoViRe: Automated Visage Recognition Attendance System” overcomes

the problems of the existing systems as mentioned previously. It mainly incorporates Facial

Recognition to mark student’s attendance into the database.

1.2.1. Salient Features of the Proposed System:

a. Face-mapping:

Facial features of the student such as distance between the eyes, width of the nose, depth of the

eye sockets, the shape of the cheekbones, the length of the jaw line, etc. are registered into the

database. Students are recognized based on these stored facial features, and if a match is found,

the student is marked present and the same is updated into the database. In all other cases, the

student is recorded absent in the database.

b. Complete Automation:

The system is automated to its full potential. The algorithm runs for few initial mins of every hour

and captures the attendance of students present in class and restarts at the beginning of the next

hour until end of day.

c. Immediate Update:

Once the algorithm successfully recognizes the student, the attendance for the corresponding

student is updates into the database automatically, without any human intervention.

d. Three-step Management:

The entire system is efficiently split into three components. Firstly, the facial features are stored,

and the model is trained. Second, the student is recognized by facial recognition algorithm

“Haarcascade”. Third, the attendance is automatically updated into the database.

e. Email to Parents:

At the end of the day, after classwork is over, an email is triggered automatically to the parents

reporting the number of hours their ward attended for that particular day.

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f. Multiple Face Detection: Another advantage with this system is that, students need not form a

queue to stand in front of the camera, get themselves recognized and update their attendance. The

system has the capability of recognizing multiple faces at a time, and hence it saves time.

9

1.3 Requirements Specifications

1.3.1 Software Requirements

● Operating System: Windows Environments

● Browser: Any efficient fast-paced modern-day browser

1.3.2 Hardware Requirements

● Processor: Pentium IV Processor or higher

● Hard Disk: 400Mb minimum hard disk storage

● RAM: 512Mb or more

1.3.3 Developer Requirements

● Operating System: Windows Environments

● Language: Python 3, HTML, CSS

● Package Installer: OpenCV 3.0, Matplotlib, NumPy, python-csv, Pillow.

● Server Base: Local Host

● IDE: Django version 2.1.7

● RAM: 2Gb Minimum

● Processor: Pentium IV Processor or higher

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2. Literature Survey

2.1. Previously Implemented Techniques for Attendance Systems

2.1.1. REAL TIME LOCATING SYSTEM USING RFID FOR INTERNET OF THINGS

In the present, attendance techniques are usually supplemented manually, because the number of

university students is increasing within training institutes, the problem with getting hold of a hand

requires human effort to report and maintain student attendance. Consequently, human errors are

common in this process. In recent years, there has been an increase in the number of applications

based on RFID (Radio Frequency Identification) systems. RFID technology facilitates automatic

wave identification using passive and active passive electronic labels with convenient readers. In

this paper, an attempt has been made to address the problem of continuous attendance of lectures

in developing countries and to find the location of special students using RFID technology. The

implementation of RFID for attending student attendance as developed and deployed in this study

is capable of eliminating lost time during manual attendance gathering and an opportunity for

education administrators to capture classroom statistics for sharing appropriate outcomes

Attendance and for further managerial decisions.

2.1.2 IOT BASED AUTOMATIC ATTENDANCE MANAGEMENT SYSTEM

In recent days, we have seen a sudden increase in the usage of Radio Frequency

Identification(RFID) systems in the fields of industrial technologies, health, agriculture,

transportation, etc. Also, Internet of Things is blooming parallelly. Therefore, using these, an

attempt has been made to solve the attendance management and monitoring problems. Attendance

Management System is the implementation of Internet of Things through Raspberry Pi 3 and RFID

Technology in order to reduce the time consumed by the traditional system of recording daily

attendance in schools and institutions. So, everything here in turn gets automated. An attempt has

also been made to develop an Android application(app) and help the students to view their

attendance anywhere, anytime.

2.1.3 AUTOMATED ATTENDANCE SYSTEM USING HAARCASCADE : A FACE

RECOGNITION APPROACH

A face recognition system is computer application capable of identifying a person from a digital

image. One of the ways to do so is by comparing selected facial features from image and a facial

database. Face recognition is used in different areas, to name a few, The Australian and New

Zealand customs services have an automated border processing system called SMARTGATE that

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uses facial recognition. The system compares the face of the individual with image in the e-

passport microchip to verify that the holder of passport is rightful owner. Properly designed

systems installed in airports, multiplexes and other public places can identify individuals among

crowd, without passers-by even being aware of the system. Other biometrics like fingerprints, iris

scans and speech recognition cannot perform this kind of mass recognition. Another area includes

ATM and check cashing security. The software is able to quickly verify a customer’s face. After

a customer consents, the ATM captures a digital image of him. The facelt software then generates

a face print of the photograph to protect customers against identity theft and fraudulent

transactions. Thus, by face recognition software there is no need for a picture ID, bankcard or

personal identification number(PIN)To verify customers identity. But it is good enough to be

already implemented in different vertical markets such as commercial sectors, healthcare and

hospitality. However, effectiveness of facial recognition software in cases of railway and airport

security is questionable as it struggles to perform under certain conditions such as lightning,

sunglasses, long hair and other objects partially covering the subjects face. Also, if the face is

pointing the camera at an angle.

2.1.4 FACE RECOGNITION BASED ON CONVOLUTION NEURAL NETWORK

In this paper, a face recognition method based on Convolution Neural Network (CNN) is

presented. This network consists of three convolution layers, two pooling layers, two full-

connected layers and one Softmax regression layer. Stochastic gradient descent algorithm is used

to train the feature extractor and the classifier, which can extract the facial features and classify

them automatically. The Dropout method is used to solve the over-fitting problem. The

Convolution Architecture for Feature Extraction framework (Caffe) is used during the training and

testing process. The face recognition rate of the ORL face database and AR face database based

on this network is 99.82% and 99.78%.

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2.2. Tabular Representation

S.No YEAR AUTHOR TITLE TECHNIQUES ADVANTAGES DISADVANTAGES

1. 2017 T.Saimounika

K.Kishore

Real Time

Locating

System using

RFID for

Internet of

Things

It uses RFID to

mark student

attendance.

Reduces

documents and

save time.

Initial cost of

installation is

required.

2. 2017 Sri Madhu

B.M

Kavya

Kanagotagi

Devansh

IoT based

Automatic

Attendance

Management

System

It uses RFID to

mark student

attendance and is

based on

Raspberry Pi.

Increased

security and

confidentiality.

Implementation of

Raspberry Pi requires

a special Operating

System.

3. 2017 Abhish Ijari

Anand

Mannikeri

Vinod Kumar

Gulmikar

Automated

Attendance

System using

Haarcascade :

A Face

Recognition

Approach

Uses Facial

Recognition to

mark student’s

attendance.

Reduces risk of

duplicate entries

or manipulations.

Camera keeps

capturing video

continuously for a

long time.

4. 2017 Kewen Yan

Shaohui

Huang

Yaoxian Song

Wei Liu

Neng Fan

Face

Recognition

Based on

Convolution

Neural

Network

Uses Facial

Recognition to

mark student’s

attendance and is

based on CNN.

Increases

accuracy of

facial

recognition.

Needs a special server

to implement CNN

and is costly.

Table 2.1: Tabular Representation of the Background Research

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3. AutoViRe: Automated Visage Recognition Attendance System

3.1. Modules Description

The entire system “AutoViRe: Automated Visage Recognition Attendance System” is

efficiently divided into four design modules namely, Feature Registration, Facial

Recognition, Attendance Update, and Email Report.

a. Feature Extraction:

The facial features such as distance between the eyes, width of the nose, depth of the

eye sockets, the shape of the cheekbones, the length of the jaw line, etc. are registered

into the database folder by using the facial recognition xml file from the ‘OpenCV’

python library. Once the features are registered, the model is trained using the gathered

features.

Figure 3.1: Feature Registration

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b. Facial Recognition:

The facial features are fetched from the database and the face of the student is

recognized by comparing with existing values in the database. Facial Recognition is

done using ‘OpenCV’ python library, particularly using ‘Haarcascade’ code.

Figure 3.2: Facial Recognition

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c. Attendance Update:

Once the facial recognition is done successfully, the attendance for the corresponding

student is updated into the database automatically, without any human intervention. If

a match is found from the existing database, the student is marked as present, in all

other cases the student is marked absent.

Figure 3.3: Attendance Update

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d. Email Notification:

At the end of the day, an email is triggered automatically and is sent to the parent

presenting the status of number of classes, the ward attended for that day.

Summary:

Figure 3.4: Facial Registration and Facial Recognition

The entire system can simply put into a nutshell as follows:

Initially the facial features of the student are captured based on convolutional neural networks [1],

extracted and registered into the database. Then the facial features are compared at the time of

taking attendance using the existing database images. If a match is found, the attendance for the

respective student for that corresponding hour is marked as present into the database, and in all

other cases it’s marked as absent. An email is triggered to the student’s parent that generates the

report of their ward’s attendance for the day.

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3.2 Design Methodology

3.2.1. Activity Diagram

Figure 3.5: Activity Diagram – Decision Making and Implementation

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Figure 3.5 signifies the activity flow in terms of decisions and how their implementation is

responsible for the application’s activity. The activity flow starts with capturing an input image.

The image capturing process runs into a loop until a proper input image is captured which is

suitable for facial detection and facial recognition. The captured image is pre-processed,

extraneous details and background noise is removed. The specific facial features such as distance

between the eyes, width of the nose, depth of the eye sockets, the shape of the cheekbones, the

length of the jaw line, etc. are analyzed and hoarded.

The captured input image, after being pre-processed and the facial features being extracted, is

verified with the existing database that consists of facial features recorded at the time of

registration of students’ details into the system.

If a match is found, the corresponding student is marked present, in all other cases, the student is

marked absent. This completes the whole process of marking of student attendance by

incorporating facial recognition.

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3.2.2. State Chart Diagram

Figure 3.6: State Chart Diagram - Representation of States and their transformation

Figure 3.6 defines the Representation of states and how their transitions occur. The initial state

deals with capturing of a suitable for facial detection and facial recognition. It runs into a loop until

an input image is captured successfully.

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The next state is pre-processing. In this state, extraneous details and background noise is

eliminated from the captured input image. After this, in the next state, feature extraction is done.

The specific facial features such as distance between the eyes, width of the nose, depth of the eye

sockets, the shape of the cheekbones, the length of the jaw line, etc. are analyzed and hoarded.

The state after this is the verification module. The captured input image, after being pre-processed

and the facial features being extracted, is verified with the existing database that consists of facial

features recorded at the time of registration of students’ details into the system.

If a match is found, the corresponding student is marked present, in all other cases, the student is

marked absent. This completes the whole process of marking of student attendance by

incorporating facial recognition and is the terminating state of the system.

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3.2.3. Class Diagram

Figure 3.7: Class Diagram – Representation of classes and their relationships

Figure 3.7 represents the classes and their relationships with each other, along with attributes and

operations. Person, Time, Student, Lecturer, Camera and Admin are the classes in this system.

The class ‘Person’ is a generalization of any person, such as a ‘Student’, ‘Admin’, or a ‘Lecturer’.

The ‘Person’ has attributes such as name, Roll No., Phone No, Aadhaar No., etc. It’s operations

are Login( ), Logout( ), Exit( ), and Verify( ).

The class ‘Student’ represents a subclass of ‘Person’, it could be any student. It has the attributes

such as FaceImage, and state. It has operations like Upload( ), and Verify( ).

The class ‘Admin’ represents a subclass of ‘Person’, it’s the admin of the system, who owns the

highest level of access of data in the system and the maximum power to manipulate data. It has

the attribute, AdminID. It’s operations are Add( ), Remove( ), and Manage ( ).

The class ‘Lecturer’ represents a subclass of ‘Person’, it could be any faculty. It has the attribute,

FacultyID. It has the operation Manage_Student_Attendance( ).

22

The class ‘Time’ represents the time of capturing of input image and its related details. It is related

to the class ‘Person’. It has attributes such as ClassTime, Camera and CaptureZone. It has the

operation Turn_Camera_On( ).

The class ‘Camera’ is related to the class ‘Time’ and represents a camera that captures the input

image. It has the attribute, CameraID. It has the operations such as Recognise( ), Detect ( ) and

Send( ).

23

3.2.4. Sequence Diagram

Figure 3.8: Sequence Diagram - Object Interaction and Process Execution

Figure 3.8 indicates the interaction amongst the application’s objects. There are three

objects, User, FaceDetector, and System.

The ‘User’ i.e. the student registers himself/herself into the ‘FaceDetector’ database. This data is

stored into the ‘System’. The ‘FaceDetector’ retrieves data from the ‘System’ and accesses the

input image from the ‘User’.

The ‘User’ sends the input value to the ‘FaceDetector’ and the ‘FaceDetector’ searches for a tally

in the ‘System’. If a match is found, the ‘User’ is marked present, otherwise, the student is marked

absent.

24

3.2.5. Collaboration Diagram:

Figure 3.9: Collaboration Diagram - Object Interaction and Process Execution

The figure 3.9 illustrates the relationships and interactions among objects through messages they

use for communication. There are three

objects, User, FaceDetector, and System.

The ‘User’ i.e. the student registers himself/herself into the ‘FaceDetector’ database. This data is

stored into the ‘System’. The ‘FaceDetector’ retrieves data from the ‘System’ and accesses the

input image from the ‘User’.

The ‘User’ sends the input value to the ‘FaceDetector’ and the ‘FaceDetector’ searches for a tally

in the ‘System’. If a match is found, the ‘User’ is marked present, otherwise, the student is marked

absent.

25

3.2.6. Use Case Diagram:

Figure 3.10: Use Case Diagram – Application’s Functionality based on Input Type

The figure 3.10 denotes the function of the application which is entirely dependent on the

User’s Input. There are two actors namely, ‘Student’ and ‘System Manager’.

The ‘Student’ Registers its facial features and enrolls itself into the system. The ‘System

Manager’ manages these registration details.

The ‘Student’ may enter and exit the class. The ‘System Manager’ recorded attendance based on

student’s movement in and out of class.

The ‘System Manager’ maintains system resources.

26

4. Testing including test cases and results

4.1 Test Cases

S.

No

Action

Performed

Expected Output Actual Output Remarks

1 Starting Up and

Successfully

Running Server

Runs successfully and

system checks take place

properly without errors

System checks report

syntax errors in code

and import crashes.

Inefficient Code

loops and improper

library imports.

2 Server Start-up

and Execution Server successfully starts

up and user gives

requirement-based inputs

Takes a time gap of a

few milliseconds for

the server to start and

run

Make sure you don’t

start the server

abruptly

3 Feature

Registration Registration Successful

with the help of UIDs

and stored within dataset

folder

No features registered

and hence no dataset

available for detection

Register the system

model in a well-lit

environment for

maximum accuracy

4 Face (Visage)

Detection Successful Detection of

Faces by tallying images

of the given dataset

Inefficient Face

Detection and

improper results

shown

Train the system with

an increased count of

data set

5 Setting up Port

Number Successful Port set up for

requests and responses Port Number already

in use and server

doesn’t start

Select a unique port

number for efficient

system execution

6 Model Training Efficient Training done

within specified time

period

Improper UID entered

while registering face

leading to inefficient

results

Make sure inputs are

given correctly before

registration takes

place

7 Emails regarding

Attendance Info Emails successfully sent

to the respective parent’s

email IDs as soon as

schedule is complete

Large number of

emails being sent

consecutively leads to

time delay.

Emails must be

grouped and sent with

frequent time gaps to

avoid lag 8 Django Setup Make sure all required

specifications are

followed and specified

Improper Path Details

and server command

doesn’t execute

System paths differ

uniquely, define them

accordingly

9 Database Update Successfully appended

student’s attendance

within the excel sheet

Database overload due

to improper appending

of data

Group and arrange

database based on

timely basis

10 OpenCV

extraction OpenCV installed

successfully and properly

imported

Version of OpenCV

differs from that of

NumPy and code

crashes

All required libraries

have to be installed

with proper versions

Table 4.1: Test Cases and Outputs

27

4.2 Results

4.2.1. Feature Registration

Figure 4.2: Feature Registration Screenshot

Figure 4.2 demonstrates the process of feature registration. the camera window opens for a few

seconds and captures 100 images from the video frame and stores in the database with the

respective student id. OpenCV uses Haarcascade algorithm[2] to extract features.

28

4.2.2. Facial Recognition:

Figure 4.3: Facial Recognition Screenshot

Figure 4.3 shows the outcome of facial recognition. The Python libraries including NumPy and

matplotlib help the system recognize faces by tallying with dataset. OpenCV detectmultiscale()

function detects the faces through the camera and recognises them using the existing dataset.

29

4.2.3. Attendance Update:

Figure 4.4: Digital Attendance Sheet

Figure 4.4 displays the excel sheet where upon the recognition of faces, the attendance is marked

for the respective student for that corresponding hour automatically, without any human

intervention into the excel sheet, which acts as the database on this case.

30

4.2.4. Email Notification:

Figure 4.5: Email Notification

Figure 4.5 shows that email notification being sent to parents at the end of the day. Every day, at

the end of class work session for the day, an email is sent to the student’s respective parents/

guardians notifying them with their ward's attendance for that day.

31

5. Conclusions and Future Scope

5.1 Conclusions

The Automated Visage Recognition based attendance system has the main goal of automating the

process of managing attendance and revolves around the fulcrum of Automation. The system

involves mainly three steps: Registration, Authentication and Update. Its fundamental goal is to

eliminate time consumption and the need to maintain paperwork. Since the advent of technology,

humans have progressed to evolve and adapt to changes based on their convenience. Bringing this

idea to practicality helps the common man to effectively and efficiently progress and this system

helps the whole organization to evolve and achieve what is necessary by eliminating the tedious

and iterative tasks.

5.2 Future Scope

The system is built on a combination of several technologies and has overcome most manual flaws

and thus stands apart from the existing systems. However, the database management is an area of

concern and needs to be filtered out on a cyclic and timely basis to avoid data overflow.

Furthermore, since the system is built upon the features of Machine learning, effective training

and efficient procedures must be followed to achieve 100% accuracy. Finally, we can build the

system as an integration of the current attendance systems being used in the organization in order

to achieve maximum efficiency.

32

Bibliography

[1] “Face Recognition Based on Convolution Neural Network” by Kewen Yan,

Shaohui Huang, Yaoxian Song, Wei Liu, Neng Fan, Conference: Proceedings of the 36th

Chinese Control Conference

[2] “Automated Attendance System using Haarcascade : A Face Recognition

Approach” by Abhish Ijari , Anand Mannikeri , Vinod Kumar Gulmikar, Conference:

International Journal for Research in Applied Science & Engineering Technology

(IJRASET)

[3] “IoT based Automatic Attendance Management System” by B.M Sri Madhu,

Kavya Kanagotagi, Devansh, Conference: 2017 International Conference on Current

Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

[4] “Real time locating system using RFID for Internet of Things” by T. Saimounika,

K. Kishore, Conference: 2017 International Conference on Energy, Communication, Data

Analytics and Soft Computing (ICECDS)

[5] “Classroom Attendance Using Face Detection and Raspberry-Pi” by Priya Pasumarti, P. Purna

Sekhar, Conference: International Research Journal of Engineering and Technology (IRJET), e-

ISSN: 2395-0056, p-ISSN: 2395-0072, Volume: 05 Issue: 03 | Mar-2018.

33

APPENDIX A – Source Code

1. VIEWS.PY

# collects helper functions and classes that “span” multiple levels of MVC

from django.shortcuts import render

# OpenCV-Python is a library of Python bindings designed to solve computer vision problems.

import cv2

# The OS module in Python provides a way of using operating system dependent functionality.

import os

# NumPy is the most basic yet a powerful package for scientific computing and data manipulation

in Python

import numpy as np

# Python Imaging Library is a free library for the Python programming language that adds support

for opening, manipulating, and saving many different image file formats.

from PIL import Image

# The csv module helps you to elegantly process data stored within a CSV file.

import csv

# wrappers library provided to make sending email extra quick, to make it easy to test email

sending

# during development, and to provide support for platforms that can’t use SMTP.

from django.core.mail import send_mail

# The method sleep() suspends execution for the given number of seconds.

from time import sleep

#function definition

def AutoViRe(request):

value=0

if request.method=="POST" and "student_choice" in request.POST:

new_student=request.POST.get('student')

print(new_student)

if new_student=='yes':

value=1

else:

value=3

if request.method=="POST" and "register" in request.POST:

cam = cv2.VideoCapture(0) #This will return video from the webcam on your

computer

34

# set video width

cam.set(3, 640)

# set video height

cam.set(4, 480)

# A Haar Cascade is basically a classifier which is used to detect the object for

# which it has been trained for, from the source

face_detector =

cv2.CascadeClassifier('C:/Users/HP/Desktop/AutoViRe/AutoViReapp/haarcascade_frontalface_

default.xml')

print(face_detector)

# For each person, enter one numeric face id

face_id = int(request.POST.get('student_id'))

print(face_id)

print("\n [INFO] Initializing face capture. Look at the camera and wait ...")

# Initialize individual sampling face count

count = 0

while(True):

#reading the video file being captured

ret, img = cam.read()

#convert images from one color-space to another

gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#detects various facial features mentioned in

haarcascade_frontalface_default.xml

faces = face_detector.detectMultiScale(gray, 1.3, 5)

print(faces)

for (x,y,w,h) in faces:

#draws a green rectangle at the top-right corner of image.

cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)

count += 1

# Save the captured image into the datasets folder

cv2.imwrite("C:/Users/HP/Desktop/AutoViRe/AutoViReapp/dataset/User." +

str(face_id) + '.' +

str(count) + ".jpg",

gray[y:y+h,x:x+w])

#the function cv2.imshow() is used to display an image in a

window.

cv2.imshow('Visage Capture', img)

# Press 'ESC' for exiting video

35

k = cv2.waitKey(5) & 0xff

if k == 27:

break

# Take 100 face sample and stop video

elif count >= 100:

break

# Do a bit of cleanup

print("\n [INFO] Exiting Program and cleanup stuff")

#release the camera resource

cam.release()

#destroy all created windows

cv2.destroyAllWindows()

value=2

if request.method=="POST" and "train" in request.POST:

# Path for face image database

path = 'C:/Users/HP/Desktop/AutoViRe/AutoViReapp/dataset'

#Local binary patterns Histogram (LBPH) is a type of visual descriptor used

for classification

# in opencv.

recognizer = cv2.face.LBPHFaceRecognizer_create()

detector =

cv2.CascadeClassifier("C:/Users/HP/Desktop/AutoViRe/AutoViReapp/haarcascade_frontalface_

default.xml");

# function to get the images and label data

def getImagesAndLabels(path):

#get the path of all the files in the folder

imagePaths = [os.path.join(path,f) for f in os.listdir(path)]

#create empth face list

faceSamples=[]

#create empty ID list

ids = []

#now looping through all the image paths and loading the Ids and the

images

for imagePath in imagePaths:

#loading the image and converting it to gray scale

PIL_img = Image.open(imagePath).convert('L') # grayscale

#Now we are converting the PIL image into numpy array

img_numpy = np.array(PIL_img,'uint8')

36

#getting the Id from the image

id = int(os.path.split(imagePath)[-1].split(".")[1])

# extract the face from the training image sample

faces = detector.detectMultiScale(img_numpy)

#If a face is there then append that in the list as well as Id

of it

for (x,y,w,h) in faces:

faceSamples.append(img_numpy[y:y+h,x:x+w])

ids.append(id)

return faceSamples,ids

print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")

faces,ids = getImagesAndLabels(path)

#train the model with captured images for respective id's

recognizer.train(faces, np.array(ids))

# Save the model into trainer/trainer.yml

recognizer.write('C:/Users/HP/Desktop/AutoViRe/AutoViReapp/trainer.yml')

# Print the numer of faces trained and end program

print("\n [INFO] {0} faces trained. Exiting

Program".format(len(np.unique(ids))))

value=3

if request.method=="POST" and "recognize" in request.POST:

recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read('C:/Users/HP/Desktop/AutoViRe/AutoViReapp//trainer.yml')

cascadePath =

"C:/Users/HP/Desktop/AutoViRe/AutoViReapp/haarcascade_frontalface_default.xml"

faceCascade = cv2.CascadeClassifier(cascadePath);

font = cv2.FONT_HERSHEY_SIMPLEX

#indiciate id counter

id = 0

# names related to ids: example ==> Sandesh: id=1, etc

names = ['Unknown', 'Sandesh', 'Shweta', 'Anchal Agrawal', 'Wasef

Mohiuddin', 'Prashanti', 'Alka','Huda', 'Harshika']

# Initialize and start realtime video capture

cam = cv2.VideoCapture(0)

# set video width

cam.set(3, 640)

# set video height

37

cam.set(4, 480)

# Define min window size to be recognized as a face

#minimum width

minW = 0.1*cam.get(3)

#minimum height

minH = 0.1*cam.get(4)

#set of value in the format (id,name,email id)

data =[[0,'Unknown',''],[1,'Sandesh',''],[2,'Shweta',''], [3,'Anchal

Agrawal','alkaagra74@gmail.com'],[4,'Wasef

Mohiuddin','wasef237@gmail.com'],[5,'Prashanti',''],

[6,'Alka',''],[7,'Huda',''],

[8,'Harshika','harshikamacharla@gmail.com']]

#number of lectures per day

periods=6

fieldname=['Unique ID','Name','E-mail ID']

for i in range(periods):

fieldname.append('Hour '+str(i+1))

for period in range(periods):

ret, img =cam.read()

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(

#input grayscale image.

gray,

#Parameter specifying how much the image size is reduced

at each image scale.

scaleFactor = 1.2,

# Parameter specifying how many neighbors each

candidate rectangle should have to retain it.

# This parameter will affect the quality of the detected faces:

higher value results in less

# detections but with higher quality

minNeighbors = 5,

#Minimum possible object size. Objects smaller than that

are ignored.

minSize = (int(minW), int(minH)),

)

data1=[]

for(x,y,w,h) in faces:

cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)

38

#predict the user Id and confidence of the prediction

respectively

id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

# If confidence is less them 100 ==> "0" : perfect match

if (confidence < 100):

id = names[id]

confidence = " {0}%".format(round(100 -

confidence))

else:

id = "Unknown"

confidence = " {0}%".format(round(100 -

confidence))

cv2.putText(img, str(id),(x+5,y-5),font,1,(255,255,255),2)

#cv2.putText(img, str(confidence), (x+5,y+h-5),font,1,

(255,255,0), 1)

cv2.imshow('AutoViRe Detection',img)

cv2.waitKey(10) & 0xff

data1.append(id)

print(data1)

data1=list(set(data1))

for i in data1:

data[names.index(i)].insert(period+3,"P")

for i in range(len(data)):

if len(data[i])!=period+4:

data[i].insert(period+3,"A")

sleep(2)

print(data)

cam.release()

cv2.destroyAllWindows()

cam = cv2.VideoCapture(0)

cam.set(3, 640) # set video widht

cam.set(4, 480) # set video height

# Define min window size to be recognized as a face

minW = 0.1*cam.get(3)

minH = 0.1*cam.get(4)

#open CSV in write mode to replace previous results and in append

mode to append new results

with open('StudentsDB.csv', 'w') as writeFile:

#write values into csv file in a dictionary form

39

writer = csv.DictWriter(writeFile,fieldnames=fieldname)

#write headers of csv file

writer.writeheader()

#write values into csv file

writer = csv.writer(writeFile)

#compute values for rows based on output of facial recognition

writer.writerows(data)

#close csv file

writeFile.close()

#release camera

cam.release()

#Destroy all created windows

cv2.destroyAllWindows()

print("\n [INFO] Exiting Program and cleanup stuff")

for i in range(len(data)):

#body of email

text="Dear Parent, Your Ward " +data[i][1]+ "'s Attendance for today

is: "

#subject of the email

subject = 'Regarding College Attendance'

if i==4 or i==3 or i==8:

print(data[i][1])

count=periods

for j in range(periods):

if data[i][j+3]=='A':

count-=1

print(count)

text+=str((count*100)/periods)+' %'

#send email to corresponding student's parent's email id

send_mail(subject,text,'',[data[i][2]],fail_silently=False)

#indicate end of program

print("End of Visage Detection")

return render(request,'sample.html',{"value":value})

40

2. INTERFACE.HTML

{% load static %}

<!DOCTYPE html>

<html>

<head>

<title>AutoViRe</title>

<style type="text/css">

.bg

{

background:#181515 0 no-repeat fixed;

height: 100%;

width: 100%;

}

.container

{

position: absolute;

top: 50%;

left: 50%;

width: 1200px;

height: 600px;

margin-top: -300px; /* Half the height */

margin-left: -600px; /* Half the width */

/* border:7px solid #FFC000;*/

}

.s1{

position: absolute;

}

.s11{

font-family: "Raleway", sans-serif;

font-size: 35px;

color: white;

margin-left: 320px;

margin-top: 200px;

}

.s2{

position: absolute;

}

.s22{

margin-left: 550px;

41

margin-top: 280px;

font-family: "Raleway", sans-serif;

}

.s3{

position: absolute;

}

.button {

background-color: #181515; /* Green */

border: none;

font-family: "Raleway", sans-serif;

color: white;

padding: 16px 32px;

text-align: center;

text-decoration: none;

display: inline-block;

font-size: 16px;

margin: 4px 2px;

-webkit-transition-duration: 0.4s; /* Safari */

transition-duration: 0.4s;

cursor: pointer;

position: absolute;

}

.button5 {

background-color: white;

color: black;

font-family: "Raleway", sans-serif;

border: 2px solid #555555;

margin-left: 535px;

margin-top: 350px;

position: absolute;

}

.button5:hover {

background-color: #555555;

color: white;

font-family: "Raleway", sans-serif;

position: absolute;

}

42

html { overflow-y: hidden;

overflow-x: hidden;

}

#container1{

position: absolute;

top: 50%;

left: 50%;

width: 1200px;

height: 600px;

margin-top: -300px; /* Half the height */

margin-left: -600px; /* Half the width */

/* border:7px solid #FFC000;*/

}

.r1{

position: absolute;

margin-left: 450px;

margin-top: 130px;

}

</style>

</head>

<body align="center">

<div>

<form method="POST">

{% csrf_token %}

<!-- <div class="container">

<img id="layer" src="./static/images/bgimage1.png" alt="nn" STYLE="position:absolute;

width:1200px;height:600px;">

<div class="s1">

--><h1><u>AutoViRe: Automated Visage Recognition Attendance System</u></h1>

<!-- </div> -->

</div>

{% csrf_token %}

{% if value == 0 %}

<h2><i>Do you want to register?</i><br></h2>

<input type="radio" name="student" value="yes">Yes

<input type="radio" name="student" value="no">No<br><br>

43

<input type="submit" name="student_choice">

{% endif %}

{% if value == 1 %}

Enter the Student ID:

<input type="value" name="student_id">

<input type="submit" name="register">

{% endif %}

{% if value == 2 %}

<h2><i>Train the model</i></h2>

<input type="submit" name="train">

{% endif %}

{% if value == 3 %}

<h2><i>Test the model</i></h2>

<input type="submit" name="recognize">

{% endif %}

</form>

</body>

</html>

44

3. URLS.PY

"""AutoViRe URL Configuration

The `urlpatterns` list routes URLs to views. For more information please see:

https://docs.djangoproject.com/en/2.1/topics/http/urls/

Examples:

Function views

1. Add an import: from my_app import views

2. Add a URL to urlpatterns: path('', views.home, name='home')

Class-based views

1. Add an import: from other_app.views import Home

2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')

Including another URLconf

1. Import the include() function: from django.urls import include, path

2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))

"""

from django.contrib import admin

from django.urls import path

from AutoViReapp import views

urlpatterns = [

path('admin/', admin.site.urls),

path('AutoViRe',views.AutoViRe,name='AutoViRe')

]

45

4. SETTINGS.PY

"""

Django settings for AutoViRe project.

Generated by 'django-admin startproject' using Django 2.1.7.

For more information on this file, see

https://docs.djangoproject.com/en/2.1/topics/settings/

For the full list of settings and their values, see

https://docs.djangoproject.com/en/2.1/ref/settings/

"""

import os

# Build paths inside the project like this: os.path.join(BASE_DIR, ...)

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

# Quick-start development settings - unsuitable for production

# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/

# SECURITY WARNING: keep the secret key used in production secret!

SECRET_KEY = 'hfwtt*cu4kq0i1g3e)0ij1s%wyx1q8m49yf^e1jwp=*b_$re*&'

# SECURITY WARNING: don't run with debug turned on in production!

DEBUG = True

ALLOWED_HOSTS = ['*']

EMAIL_USE_TLS = True

EMAIL_HOST = 'smtp.gmail.com'

EMAIL_HOST_USER = 'autovire@gmail.com'

EMAIL_HOST_PASSWORD = '*********'

EMAIL_PORT = 587

# Application definition

INSTALLED_APPS = [

46

'django.contrib.admin',

'django.contrib.auth',

'django.contrib.contenttypes',

'django.contrib.sessions',

'django.contrib.messages',

'django.contrib.staticfiles',

'AutoViReapp',

]

MIDDLEWARE = [

'django.middleware.security.SecurityMiddleware',

'django.contrib.sessions.middleware.SessionMiddleware',

'django.middleware.common.CommonMiddleware',

'django.middleware.csrf.CsrfViewMiddleware',

'django.contrib.auth.middleware.AuthenticationMiddleware',

'django.contrib.messages.middleware.MessageMiddleware',

'django.middleware.clickjacking.XFrameOptionsMiddleware',

]

ROOT_URLCONF = 'AutoViRe.urls'

TEMPLATES = [

{

'BACKEND': 'django.template.backends.django.DjangoTemplates',

'DIRS': [],

'APP_DIRS': True,

'OPTIONS': {

'context_processors': [

'django.template.context_processors.debug',

'django.template.context_processors.request',

'django.contrib.auth.context_processors.auth',

'django.contrib.messages.context_processors.messages',

],

},

},

]

WSGI_APPLICATION = 'AutoViRe.wsgi.application'

47

# Database

# https://docs.djangoproject.com/en/2.1/ref/settings/#databases

DATABASES = {

'default': {

'ENGINE': 'django.db.backends.sqlite3',

'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),

}

}

# Password validation

# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators

AUTH_PASSWORD_VALIDATORS = [

{

'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',

},

{

'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',

},

{

'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',

},

{

'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',

},

]

# Internationalization

# https://docs.djangoproject.com/en/2.1/topics/i18n/

LANGUAGE_CODE = 'en-us'

TIME_ZONE = 'UTC'

USE_I18N = True

USE_L10N = True

48

USE_TZ = True

# Static files (CSS, JavaScript, Images)

# https://docs.djangoproject.com/en/2.1/howto/static-files/

STATIC_URL = '/static/'

49

5. APPS.PY

from django.apps import AppConfig

class AutovireappConfig(AppConfig):

name = 'AutoViReapp'

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6. WSGI.PY

"""

WSGI config for AutoViRe project.

It exposes the WSGI callable as a module-level variable named ``application``.

For more information on this file, see

https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/

"""

import os

from django.core.wsgi import get_wsgi_application

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'AutoViRe.settings')

application = get_wsgi_application()

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7. MANAGE.PY

#!/usr/bin/env python

import os

import sys

if __name__ == '__main__':

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'AutoViRe.settings')

try:

from django.core.management import execute_from_command_line

except ImportError as exc:

raise ImportError(

"Couldn't import Django. Are you sure it's installed and "

"available on your PYTHONPATH environment variable? Did you "

"forget to activate a virtual environment?"

) from exc

execute_from_command_line(sys.argv)

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APPENDIX B – User Manual

1. Creation of AutoViRe app on the Django framework

$ django-admin startproject AutoViRe

Figure B.1: Creation of Django project

This creates the Django project and generates a command-line utility that lets you interact with

this Django project in various ways the manage.py file. An initialization file __init.py__,

settings.py file that deals with app settings and configurations. A urls.py file that maps the URLs

and functions to each other i.e. the URL declarations for this Django project; a “table of contents”

of your Django-powered site. The wsgi.py file which is an entry-point for WSGI-compatible web

servers to serve your project.

In settings.py file, modify settings.py file as follows:

Change the ‘Databases’ class to the code below:

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Figure B.2: Databases class

Change ‘TimeZone’ class to the code below:

Figure B.3: Time_Zone class

Then on the command prompt, execute $ python manage.py migrate

Figure B.4: Making database migrations into the project

The migrate command looks at the Installed_Apps setting and creates any necessary database

tables according to the database settings in your mysite/settings.py file and the database

migrations shipped with the app. You’ll see a message for each migration it applies.

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On the command prompt, execute $ python manage.py runserver

Figure B.5: Running the localhost server

On the command prompt, execute $ python manage.py startapp autovireapp

Figure B.6: Creation of django app

This creates the app in the project that can run the functionalities of the project.

In the settings.py file, change the ‘Installed_Apps’ class to the code below:

Figure B.7: Installed_Apps class

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Add ‘autovireapp’ name in the end after all other default apps.

On the command prompt, execute $ python manage.py makemigrations autovireapp

Figure B.8: Making migrations into Django App

On the command prompt, execute $ python maange.py createsuperuser to create a user who can

login to the admin site.

Figure B.9: Creation of Super User for Admin

On the command prompt, execute $ python manage.py runserver

Figure B.10: Running of localhost server

On the internet browser, type the URL: http://127.0.0.1:8000/admin

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Figure B.11: Admin page login for the Django localhost

Now, try logging in with the superuser account you created in the previous step. You should see

the Django admin index page:

Figure B.12: Django project Admin Page

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2. Redirection to Project on command line

Figure B.13 Redirecting to Project Path

Figure B.13 represents the Command Prompt in which the user redirects to the Project Location

on the system from the root. This is done in order to run the server on which the application has

been built. Only after redirection takes place successfully, the user can run and execute the

application on the server.

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3. Startup of Server

Figure B.14 Running Server on Command Line

Figure B.14 represents the execution of server on command line. The command ‘runserver’

performs system checks to check whether the environment is set up properly and all the

configurations are properly maintained. It also starts the development server and indicates the

domain address.

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4. Execution on Browser

Figure B.15 Browser Display of Application Execution

Figure B.15 represents the display of the homepage of application execution. This is the first page

of the application and asks the user if he/she wants to register the face with the system. Based on

the input given, the application displays the corresponding page accordingly.

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5. Unique Identification assigned to Visage

Figure B.16 Registration based on Unique Student ID

Figure B.16 describes the display of a numeric entry asking for a unique ID based on which the

dataset would be classified. Based on what entry is given, the system registers the face under that

identification number. This ensures easy, effective and efficient classification.

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6. Visage Capture

Figure B.17 Facial Registration

Figure B.17 represents the face being recognized by the system and is stored under the unique ID

given in the previous step. Based on the count of frames given in the code, the snapshots are taken

and stored for each unique identifier.

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7. Dataset Storage

Figure B.18 Dataset consisting of several registered faces

Figure B.18 represents the dataset that contains images of all faces that have been registered into

the system. This is a very integral part of the system, since its responsible for both training and

testing the system model.

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8. Training the Model

Figure B.19 Browser Display of Training Webpage

Figure B.19 represents the display of the training webpage which triggers the execution of the

training of registered faces within the dataset.

Figure B.20 Training Display on the command line

Figure B.20 represents the process of training of model which helps the model achieve maximum

accuracy and efficiency every time its run and executes based on number of faces.

Figure B.21 Completion of Training

Finally, Figure B.21 represents the completion of successful training after a certain time and this

results in the model being ready to successfully execute and get tested.

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9. Testing Model

Figure B.22 Browser Display of Final Testing

Figure B.22 represents the final webpage which executes the main part of the application and

detects faces based on the dataset. This step also involves immediate updating of database along

with an email being sent to the respective parents.

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10. Face Detection

Figure B.23 Successful Face Detection based on Dataset

Figure B.23 indicates the faces being detected and their corresponding names as well, this is

automatically updated in the database behind the scenes and this executes on an iterative basis

until the schedule for that particular day comes to an end.

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11. Attendance Capture on an hourly basis

Figure B.24 Final Tally of Attendance for the current day

Figure B.24 represents the count of the number of hours each student is present in class during that

hour. This is displayed on the command line and is also updated within the database immediately

for each hour.

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12. Database Updating based on timely capture

Figure B.25 Final Updated Database

Figure B.25 represents the updated database with all the values being marked throughout the day

for the respective students. This eliminates the huge paperwork which is normally done when

manually managing attendance.

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13. Email sent to the respective parents

Figure B.26 Email sent to the Respective Parent

Figure B.26 represents the email which has been sent to the respective parent indicating their

ward’s attendance for the day. This is also an automated addition to the system which executes on

its own after the database has been updated.