An Investigational Study on Face Recognitionbased Attendance Tracking System for Educational Institutions

In recent years, technological advancements have brought about transformative changes in various educational domains, notably in attendance management. Conventional methods for tracking student attendance are often cumbersome and susceptible to proxy interventions. Face recognition technology has em...

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Bibliographic Details
Main Authors: J V, Srivaishnavi, Sathrukkan, Adwaita, Bindu, Avadhani, S, Raghul, Kumar, T Senthil, Jeyakumar, G
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Attendance management system
Behavioural analysis
Computer architecture
Convolutional neural networks
Deep learning
Emotion recognition
Face detection
Face recognition
Feature extraction
Manuals
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Summary:In recent years, technological advancements have brought about transformative changes in various educational domains, notably in attendance management. Conventional methods for tracking student attendance are often cumbersome and susceptible to proxy interventions. Face recognition technology has emerged as a promising solution to enhance this system within educational institutions. The investigational study presented in this paper aims at designing and implementing a face recognition-based system to automate the manual attendance taking process of educational institutions. This study is encompassed with two phases. The Phase I is for an analytical study to identify a best face recognition model. The Phase 2 is to design and implement an attendance tracking system incorporated with the best model identified in Phase I. A comparative study with five state-of-the-art face detection models, in the Phase I, revealed that the Caffe (Convolutional Architecture for Fast Feature Embedding) model as a best suited one for further experiments in this study. The Phase II proposes a system, named as AAIRAA (Amrita's AI-based Recognition and Analytical Application), integrating the Caffe model. The proposed AAIRA system is equipped with the features of facial recognition, emotion detection and fall detection. This study also incorporates user level testing of the AAIRA system, in an educational institution, which have proved its potential in streamlining the administrative tasks, diminishing the potential for fraudulent attendance and tracking of attendance.
ISSN:2767-7788
DOI:10.1109/ICICT60155.2024.10544419