Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{207167,
        author = {Kanisha Nekadi and Prof. Snehlata Mishra},
        title = {A Hybrid Machine Learning Model for Efficient and Spoof-Resistant Face Recognition Attendance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {49-55},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207167},
        abstract = {In educational institutions and organizations, the automatic attendance system using face recognition systems has proven to be a beneficial tool for attendance automation. But normal systems are susceptible to the spoofing attacks like printed photographs, replay videos, and images from the mobile screens and makes the system less reliable. This paper introduces a Hybrid Machine Learning Model for Efficient and Spoof-Resistant Face Recognition Attendance model which includes four stages: MTCNN (Face Detection), Facenet (Face Feature Extraction), Random Forest (Face Classification), and Anti-Spoofing module (Liveness Detection). In real-time, the system takes facial pictures, does preprocessing, checks user identity, finds the spoofing, and records attendance automatically into a secure database. The proposed framework has better recognition accuracy, better security level and lower false acceptance rate than the traditional face recognition system. The parameters measured for performance are Accuracy, Precision, Recall, F1-Score, False Acceptance Rate (FAR) and False Rejection Rate (FRR). The envisioned model offers a safe, scalable, and efficient attendance management system for educational institutions, corporate entities, and smart workplaces},
        keywords = {—Face Recognition, Machine Learning, FaceNet, MTCNN, Anti-Spoofing, Liveness Detection, Attendance System, Computer Vision.},
        month = {August},
        }