project

1. Mr Dr Sivasubramaniam M.Tech.,Ph.d.,ADBA.,DCP LAVANYA.E [411420205028]
JEEVA.G [411420205020]
JAYASUTHA.N [411420205018]
PRIYADHARSHINI.A [411420205042]

2. In this project we have implemented the automated attendance system using Machine Learning.
We have projected our ideas to implement “Automated Attendance System Based on Facial
Recognition”, in which it imbibes large applications. The application includes face identification,
which saves time and eliminates chances of proxy attendance because of the face authorization.
Hence, this system can be implemented in a field where attendance plays an important role.
There are some automatic attendance taking systems which are currently being used by
multiple institutions. Example of one such system is the use of biometric technique. Although it is
automatic and a step ahead of the traditional method, it fails to meet the time constraint. The
student has to wait in queue for giving attendance, which is time taking. This project introduces an
involuntary attendance marking system, devoid of any kind of interference with the normal teaching
procedure. An automatic attendance system by facial recognition using machine learning is a smart
and organized way for any organization which demands the regular maintenance of the attendance
of the employees, worker or students

3. Image processing is a method to perform some operations on an image, in order to get an
enhanced image or to extract some useful information from it. It is a type of signal processing in
which input is an image and output may be image or characteristics/features associated with that
image.
Haar Cascade is a machine learning object detection algorithm used to
identify objects in an image or video.
The algorithm has four stages:
1.Haar Feature Selection
2.Creating Integral Images
3.Adaboost Training
4.Cascading Classifiers

4. System : Pentium IV 2.4 GHz.
Hard Disk : 1TB
Floppy Drive : 1.44 Mb.
Monitor
Mouse : Logitech
Ram : 8GB
: 15 VGAcolor
Operating system : Windows 10
Front End : Html
Bank End : Sqllite3
Coding Language : Python
IDE : Anaconda Navigator

5. USECASE DIAGRAM
Capturing
Student
Face
Apply Haar
Cascade
algorithm
Face Detection
and Recognition
Module
Result
Update the
attendence in
database

6. ❖ Time-saving
❖ Increased accuracy
❖ Real-time monitoring
❖ Convenience
❖ Security
❖ Cost
❖ Complexity
❖ Technical limitations
❖ Privacy concerns
In this system we have implemented an attendance system for a lecture, section or laboratory by which
lecturer or teaching assistant can record students’ attendance. It saves time and effort, especially if it is a
lecture with huge number of students. Automated Attendance System has been envisioned for the purpose
of reducing the drawbacks in the traditional (manual) system. This attendance system demonstrates the
use of machine learning techniques in classroom. This system can not only merely help in the attendance
system, but also improve the goodwill of an institution. Students using mobile phone or not attend class
mentally means mark will be Reduced. For future work, the plan is to use neural network based face
recognition in order to speed up the process.
.

7. 1)Shireesha Chintalapati; M. V. Raghunadh, "Automated Attendance Management System Based On Face
Recognition Algorithms", 2013 IEEE International Conference on Computational Intelligence and
Computing Research.
2)Abhishek Jha, “Class Room Attendance System Using Facial Recognition System”, IEEE The
International Journal of Mathematics, Science, Technology and Management (ISSN : 2319-8125) Vol. 2
Issue 3,2015.
3)N.Sudhakar Reddy, M.V.Sumanth, S.Suresh Babu, "A Counterpart Approach to Attendance and
Feedback System using Machine Learning Techniques",Journal of Emerging Technologies and
Innovative Research (JETIR), Volume 5, Issue 12, Dec 2018.
4)Dennis Haufe, Rolf P.Wiirtz and Manuel Gunter “Face Recognition With Disparity Corrected Gabor
Phase Differences” pp. 411–418, Springer-Verlag, 2012.

8. Thank you