Dr. Chandrakanth G Pujari (Professor)
Department of Information Science and Engineering
Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
Email- lavbangalore6.is@drait.edu.in

Amith V Hiremath
Department of Information Science and Engineering
Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
Email- 1da17is048.is@drait.edu.in

Sirisha R
Department of Information Science and Engineering
Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
Email- 1da18is405.is@drait.edu.in

Brundha P
Department of Information Science and Engineering
Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
Email- 1da18is400.is@drait.edu.in

Guruprasad K N
Department of Information Science and Engineering
Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India
Email- 1da18is402.is@drait.edu.in

Abstract- In this article, the student attendance management system is used to track and monitor the presence of a student in a class. The attendance system using facial-recognition is safer and saves time. This research focuses on an attendance system based on facial-recognition by obtaining a lower wrong-positive rate by using a threshold to confidence i.e., Euclidian distance value while detecting strange people and saving their pictures. The Local Binary Pattern Histogram (LBPH) algorithm is better than other Euclidean algorithms such as Eigenfaces and Fisherfaces. We used Haar cascade algorithm for facial detection based on its robustness and its LBPH algorithm for facial recognition. We have an 80% face recognition rate among students and a 28% wrong-positive rate. This system recognizes students even with glasses or beards. The Facial recognition for strange persons is almost 80% with and without applying the threshold value. Its wrong-positive ratio is 17% and 40% with and without the thresholds respectively.

Keywords – Face detection, Face recognition, LBPH algorithm, Haar Cascade.


Maintaining attendance is mandatory and important in all the institutions for knowing the performance of students. In every institution the teacher or lecturer takes the attendance manually. For ex. If the duration of class of one subject is about 50 minutes and teacher take 5-10 minutes to mark the attendance. It will be time consuming. And students can get proxy easily by their friends even if they are absent. To avoid proxy, teacher has to call out the names of the student and look at the faces each time so even this consumes time. Maintaining manual attendance is not secure and it can be lost.
To overcome this problem, we used Haar cascade for facial detection based on its robustness and its LBPH algorithm for facial recognition. Every human being has a unique face and fingerprints. From these features we can easily identify a person. Our proposed system uses face as the main element to recognize the students and mark the attendance. And the attendance sheet will be generated automatically and stored in the database. Even parents will get message if the pupil is absent. It maintains a separate report of students if they are absent. The teacher can fetch the details of the students whenever they want.
The main aim of our proposed system is to create a attendance system based on face recognition with minimum wrong-positive rate in recognizing strange persons by applying a scale factor and save their images. It is robust against monotonic gray scale
transformations. This attendance system will also detect and save the images of any strange person in the classroom whose student details is not present in the database.


In this proposed automatic attendance management program, we used the Haar Cascade algorithm to detect faces and the LBPH face recognition algorithm. The Graphical User Interface (GUI) screen for this program is shown in Fig. 3 was created using the Python module Tkinter package which is a quick and easy way to create a GUI system.
The program will provide functionality such as photographing students and their student id, name and phone number and stores in the database, train the photos stored in the database and begin to identify students present in the classroom. When the teacher wants to note the presence of students, the program will see the faces of the students present in the classroom from the camera and process it before identifying further. The sections of the proposed plan are shown in

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