OBJECTIVE

• This project describes multiple attendance system using face recognition

• An robust and efficient face detection based attendance system is implemented

• Face recognition widely implemented for recognize the face

• Time is gold as the famous line goes, time is very precious thing in today‘s fast changing, fast developing
environment, through the different stages of civilizations man always tried to look for ways to make his work
easier.

• Firstly employees fingerprint are scanned by software and an identity number is allotted as their
enrollment .During the attendance time when employees impress their fingerprints against the scanner
LITERATURE SURVEY
S.No TITLE AUTHOR DESCRIPTION

1
Automated Jenif D Souza W S To make the attendance marking and management system
Attendance Marking Jothi S Chandrasekar fully automatic, simple and easy. In this work the facial
and Management A recognition of face is done by image processing
System by Facial 2019 techniques
Recognition Using
Histogram

2
Automatic E.Varadharajan,R.Dh The camera is fixed in the classroom and it will capture
Attendance arani, S.Jeevitha, the image, the faces are detected and then it is recognized
Management System B.Kavinmathi, with the database and finally the attendance is marked. If
Using Face Detection S.Hemalatha the attendance is marked as absent the message about the
2020 student’s absent is send to their parents.
S.No TITLE AUTHOR-YEAR DESCRIPTION

3
Attendance Nazare Kanchan An automated Attendance Management System (AMS)
Management System Jayant Surekha based on face detection and face recognition techniques is
Using Hybrid Face Borra proposed in this paper. The system employs modified
Recognition 2019 Viola-Jones algorithm for face detection, and alignment-
Techniques free partial face recognition algorithm for face
recognition.

4
Smart Attendance Swarnendu Ghosh, The Android application is only accessible by the
System Mohammed Shafi authorized personnel to monitor the student attendance
KP, Neeraj Mogal, and to share for academic record. The device is highly
Prabhu Kalyan secured as it can only be activated by the fingerprint
Nayak, Biswajeet recognition of the concerned authorized personnel
Champaty (faculties).
2019
S.No TITLE AUTHOR-YEAR DESCRIPTION

5
Automated S. Aravindh, R. We can implement an effective system which will mark
Attendance Athira, M. J. Jeevitha the attendance of students automatically by recognizing
Managementand their faces. The process of this face recognition system is
Reporting System 2020 divided into various steps, but the important steps are
using Face detection of face and recognition of face. Firstly, to mark
Recognition the attendance of students, the image of students' faces
will be required.
6
Class Attendance Clyde Gomes An automated attendance marking and management
Management System Sagar Chanchal system is proposed by making use of face detection and
using Facial Tanmay Desai recognition algorithms. Instead of using the conventional
Recognition 2019 methods, this proposed system aims to develop an
automated system that records the student’s attendance by
using facial recognition technology.
S.No TITLE AUTHOR-YEAR DESCRIPTION

7
Face Recognition Mohd Abdul Muqeet To develop an attendance management system that uses
Based Attendance 2019 the face of students as the feed input. To make it available
Management System for every platform we have chosen the Raspberry Pi and a
Using Raspberry Pi webcam. A webcam is linked with the Raspberry Pi
module.

8
A Smart Attendance Harish M; Chethan A new way to take attendance of students in a classroom,
System based on P; Prajna N Holla K; which is efficient, less time consuming and which can be
Machine learning Syed Abdul Azeem done using devices that are readily available with people
2020 in today's day and age such as smartphones,
laptops/desktops. In the proposed model, the power of
Machine learning and versatility of Google Drive have
been put to good use to build a smart attendance system.
 EXISTING SYSTEM

• AdaBoost learning algorithm has been implemented.

• Cascade classifier algorithm has been implemented for face recognition.

• Machine learning algorithm has been implemented

• Less accuracy
• Less sensitivity
• It extract the reduced number of features
Proposed system
• The proposed system Viola Jones Framework Algorithm implemented for face recognition group
face image attendance system .

• This project is live attendance system using face recognition

• a smart, single input, multiple outputs, attendance system based on face recognition algorithms.
This system overcomes the problems faced while using traditional systems.
Viola Jones Framework Algorithm

• This is a Paradigmatic method for Real time Face detection. Training is slow, but
detection is very fast. The task of face detection in Viola Jones algorithm uses a
24x24 window as the base window size to start evaluating these features in any
given image.
Block diagram
Camera for capture live Video into frames Image
Create database
video conversion preprocessing

CNN training
Attendance system
Register data base

Video into frames

• Dataset Creation

• Image preprocessing

• CNN Architecture

• Model development

• Attendance system
Dataset Creation

• In this module, the dataset for the student is created by using Opencv-
python.

• One thousand image where collected from each and every students.
Image Preprocessing

• Image preprocessing stages such as gray scale conversion, Image

• Developed model is deployed on the face detection code to monitor

 H/W System Configuration:-

 RAM - 4 GB (min)

 Hard Disk - 20 GB

 S/W System Configuration:-

 Tools: python, Jupiter notebook, anaconda tool

• The proposed automated attendance system using face recognition is a great model
for marking the attendance of students in a classroom.

• This system also assists in overcoming the chances of proxies and fake attendance.
In the modern world, a large number of systems using biometrics are available.

• However, the facial recognition turns out to be a viable option because of its high
accuracy along with minimum human intervention. This system is aimed at
providing a significant level of security.
REFERENCES
[1] T. Sutjarittham et al., “Data-Driven Monitoring and Optimization of Classroom Usage in a Smart Campus,” in

Proc. ACM/IEEE IPSN, Porto, Portugal, 2018.

[2] B. Council, “The shape of things to come: higher education global trends and emerging opportunities to 2020,”

British Council, Tech. Rep., 01 2012.

[3] I. P. Mohottige et al., “Estimating Room Occupancy in a Smart Campus Using WiFi Soft Sensors,” in Proc. IEEE

LCN, Chicago, USA, October 2018.

[4] B. Dong, B. Andrews, K. P. Lam, M. H¨o Ynck, R. Zhang, Y. Chiou, and D. Benitez, “An information technology

enabled sustainability testbed (itest) for occupancy detection through an environmental sensing network,” Energy and

Buildings, vol. 42, no. 7, pp. 1038–1046, July 2010.

[5] A. Dey, X. Ling, A. Syed, Y. Zheng, B. Landowski, D. Anderson, K. Stuart, and M. E. Tolentino, “Namatad:

Inferring occupancy from building sensors using machine learning,” in Proc. IEEE WF-IoT, Reston, VA, USA, 2016.
 WORK PLAN

S NO. Module description Target date

JANUARY LAST WEEK

FERUARY MONTH
3 Testing and debugging

MARCH MONTH
4 Thesis preparation
THANK YOU