Cloud Based Attendance Automation System With Analytics And Reporting

Abstract

Managing attendance has always been tedious for the organizations as it is time consuming and repetitive activity. Fake attendance is a major issue to be addressed. Existing bio-metric system is a decent solution but they have limited functionality and have many vulnerabilities. Our objective is to provide an efficient and scalable cloud-based solution with facial recognition which can detect fake attendance and provide alerts to the management in case of irregular attendance. Our solution follows a verification process to avoid fake attendance. Initially, students have to upload a photo with their ID cards into our application. Next, the teacher or supervisor of the class will take a group photo of all the students and upload them into cloud through a mobile application, which will act as a user interface of our solution. The cloud architecture deployed in our solution combines various services like Amazon Rekognition, Amazon Simple Storage Service, Amazon Relational Database Service, Amazon Simple Notification Service, Amazon Lambda, Amazon Textract and Amazon Amplify in the most efficient manner in terms of storage and cost by regular deletion of unwanted data. We can then use the required cloud services to compare the faces of the student’s photo saved in our database with the group photo uploaded by the teacher to detect the students who are present. The proposed solution also analyzes the historical data within the database to prepare an aggregated report that consists of various fields like frequency of irregularity in attending classes, subject balance score and average attendance. While the back-end of the application is deployed in cloud, the front-end will be handled using Android as 70% of the general populace are dependent on this platform. Our solution uses Model-View-View-Model (MVVM) architecture. Execution time in MVVM applications is faster due to it supporting data binding with an average difference of 126.21ms.