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@article{197251,
        author = {Atharv Parit and Vrushank Patil and Krishnal shinde and Pranav yadav},
        title = {AI BASED ATTENDANCE SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10089-10093},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197251},
        abstract = {Attendance management is a fundamental administrative task in educational institutions. Conventional methods such as manual roll calls and biometric systems are time-consuming, error-prone, and susceptible to proxy attendance. Moreover, biometric systems raise hygiene and accessibility concerns. With the rapid advancement of artificial intelligence and computer vision, face recognition has emerged as an effective, contactless, and automated solution for attendance management.
This paper presents the design and partial implementation of an AI-based student attendance system using deep learning-based face recognition techniques. The primary focus of this work is on the registration phase, where facial images of students are collected, processed, and converted into numerical embeddings. Face detection and alignment are performed using the Multi-task Cascaded Convolutional Neural Network (MTCNN), and facial embeddings are generated using the Face Net model. These embeddings are securely stored in Firebase Fire store along with student metadata.
The recognition and attendance marking phase, which involves real-time video processing, face matching, automatic attendance marking, and report generation, is proposed as future work. The proposed system aims to provide a scalable, accurate, and contactless attendance solution suitable for modern educational environments.},
        keywords = {AI-Based Attendance System, Face Recognition, MTCNN, Face Net, Deep Learning, Firebase Fire store, Computer Vision},
        month = {April},
        }