FACIAL AUTOMATED ATTENDANCE SYSTEM - Datapro Consultancy Services

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ABSTRACT

FACIAL AUTOMATED ATTENDANCE SYSTEM is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is a difficult process, especially for a large group of students. The conventional method of calling the name of each student is time consuming and there is a chance of proxy attendance. The following system is based on face recognition to maintain the attendance record of students. The daily attendance of students is recorded subject wise which is stored already by the administrator. As the time for corresponding subjects arrives the system automatically starts taking snaps and then applying face detection and recognition techniques to the given image and the recognized students are marked as present and their attendance updated with corresponding time and subject id.

FACIAL AUTOMATED ATTENDANCE SYSTEM system will be implemented with 5 phases such as Image capturing, Face Detection, Feature Extraction, Face Recognition and updating of attendance in the database. Our system is capable of identifying multiple faces in real time. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. For the case of Face Detection, we opted for the Haar Cascade algorithm, while for the case of Face Recognition, we opted for the LBPH algorithm. We can view the attendance in the form of an excel sheet.

Keywords: Face Detection, Face Recognition, Harr cascade, LBPH, Pre-processing, Attendance.

INTRODUCTION

A key factor of improving the quality of education is having students attend classes regularly. Maintaining the attendance is very important in all the institutes for checking the performance of students. Traditionally students are stimulated to attend classes using attendance points which at the end of a semester constitute a part of a student’s final grade. However, this presents additional effort from the teacher, who must make sure to correctly mark attending students, which at the same time wastes a considerable amount of time from the teaching process and there is a chance for proxy attendance. Furthermore it can get much more complicated if one has to deal with large groups of students. Maintaining an automated attendance management system ensures us with accurate attendance avoiding proxy attendances. Every institute has its own method in this regard. Some are taking attendance manually using the old paper or file based approach and some have adopted methods of automatic attendance marking using some biometric techniques. There are many automatic methods which are available for this purpose like biometric attendance. All these methods take a lot of time because students have to make a queue to touch their thumb on the scanning device. This system uses the face recognition approach to mark attendance of students in the classroom environment without student’s intervention. This attendance is recorded by using a camera fixed in a classroom which captures images, detects student faces in the image and compares the detected faces with the images in the database and marks the attendance. We can view the attendance in the form of csv file.

Automated Attendance Management System Based On Face Recognition Algorithms

On this paper they propose an automated attendance management system. This system is basically based on face detection and recognition algorithms, automatically detecting the student when he enters the classroom and marks the attendance by recognizing him. Because LBPH outperforms other algorithms with better recognition rate and low false positive rate the system is based on this algorithm. The system uses SVM and Bayesian as a classifier because they are better when compared to distance classifiers. The workflow of the system architecture is when a person enters the classroom his image is captured by the camera at the entrance. A face region is then extracted and pre-processed for further processing. As not more than two persons can enter the classroom at a time, face detection algorithm has less work. The future work they are saying on this paper is to improve the recognition rate of algorithms when there are unconscious changes in a person like tonsuring head, using a scarf, facial hair. The limitation of the system is it only recognizes face up to 30 degrees angle variations which have to be improved further. Gait recognition should be combined with face recognition systems in order to achieve better performance of the system.

HAAR CASCADE ALGORITHM

It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. The algorithm can be explained in four stages:

• Calculating Haar Features:

The first step is to collect the Haar features. A Haar feature is essentially calculations that are performed on adjacent rectangular regions at a specific location in a detection window. The calculation involves summing the pixel intensities in each region and calculating the differences between the sums. Each feature is a single value obtained by subtracting sum of pixels under white rectangle from sum

of pixels under black rectangle. Here are some examples of Haar features below.

Title: FACIAL AUTOMATED ATTENDANCE SYSTEM

Abstract:
This postgraduate student project aims to develop a Facial Automated Attendance System using Python and web technologies. The system leverages facial recognition technology to streamline attendance tracking in educational institutions or corporate environments. The project encompasses the analysis of the existing manual attendance systems, proposing an automated alternative, defining system requirements, outlining algorithms, specifying hardware and software requirements, and detailing the system architecture and web user interface.

Existing System:
Traditional attendance systems involve manual processes, such as roll-calls or signature-based sheets, which are time-consuming, prone to errors, and lack efficiency. These methods are not scalable, especially in large organizations or classrooms, leading to the need for a more accurate and automated solution.

Proposed System:
The proposed system introduces a Facial Automated Attendance System that utilizes facial recognition algorithms to identify and authenticate individuals, automatically marking their attendance. This system eliminates the need for manual intervention, providing a seamless and efficient way to track attendance. The project aims to improve accuracy, reduce administrative overhead, and enhance overall attendance management.

System Requirements:

High-resolution cameras for facial image capture
Sufficient computational resources for real-time image processing
Database for storing and managing facial data
Web server for hosting the application
Internet connectivity for remote access

Algorithms:
The project employs state-of-the-art facial recognition algorithms, such as OpenCV for face detection and recognition, and possibly deep learning models like Convolutional Neural Networks (CNNs) for enhanced accuracy.

Hardware and Software Requirements:

Hardware: Cameras, computer/server with ample processing power, storage, and memory.
Software: Python programming language, OpenCV library, a web framework (e.g., Django or Flask), a database system (e.g., MySQL or PostgreSQL).

Architecture:
The system follows a client-server architecture, where the client-side involves capturing facial images, processing them locally, and sending the data to the server. The server manages the facial recognition algorithms, communicates with the database, and updates the attendance records. A web interface provides administrators and users with a means to monitor attendance data and generate reports.

Technologies Used:

Python for backend development
OpenCV for facial detection and recognition
Django or Flask for web development
MySQL or PostgreSQL for database management

Web User Interface:
The web interface allows administrators to manage user profiles, monitor attendance records, and generate reports. Users can access their attendance information, providing a user-friendly experience. The interface is designed for responsiveness and ease of use, ensuring accessibility from various devices.

In conclusion, the Facial Automated Attendance System combines advanced facial recognition technology with web-based solutions to offer a more efficient and accurate method for attendance tracking, addressing the limitations of existing manual systems. The integration of Python and web technologies provides a robust and scalable platform for effective attendance management in diverse settings.