Autonomous Attendance Mobile Application Based On Face Recognition Or Voice Recognition

Abstract

In Kazakhstani universities, the process of checking student attendance is a crucial aspect of evaluating their performance and determining their final grades. Currently, the traditional method of manual attendance is time-consuming and prone to error [1]. According to the previous section, attendance information should be gathered in the classroom to confirm the presence of students and teachers. However, this is not an efficient way, since it takes up a significant amount of time to call out each student's name and mark their attendance. Moreover, this manual approach also leaves room for students to forge their attendance, either by marking their friends as present or by proxying for someone else. Consequently, there is a need for an automated system that simplifies the process of checking attendance and eliminates fraud. To overcome these drawbacks, the proposed system uses an autonomous attendance-checking Android application. This application utilizes various technologies to enhance the accuracy and convenience of attendance management [2]. Android, designed for low-power devices, provides a perfect platform for developing such applications [1]. Firebase provides multiple functions to help complete the application. In conclusion, the proposed autonomous attendance-checking Android application utilizes Firebase Authentication, Firebase Cloud Messaging, Firebase Machine Learning, TensorFlow Lite model for face recognition, Speech Recognizer class for voice recognition, File class for storing serialized files, and Firebase Realtime Database. Overall, the application leverages the power of Firebase services and machine-learning technologies to create a secure and efficient attendance-checking system. In conclusion, the project aims to create an autonomous attendance-checking Android application that utilizes various Firebase services such as authentication, cloud messaging, and a real-time database.