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@article{178563,
        author = {CHANDREYI AVIJIT GHOSH and Dr.Zafar Ali Khan N},
        title = {Face Recognition-Based Attendance Monitoring System Using LBPH and Haar Cascade for Recognition and Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3797-3801},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178563},
        abstract = {Efficient attendance management is a critical concern for organizations, particularly in educational institutions, where accurate tracking is essential for administrative and academic success. This paper presents a detailed study and implementation of an automated attendance monitoring system that leverages facial recognition technology. By utilizing Haar Cascade Classifiers for face detection and Local Binary Pattern Histogram (LBPH) for face recognition, the system achieves real-time performance and reliability [19]. The study provides experimental results, error analysis, and a thorough comparison with existing biometric solutions [1][2][12]. In addition to exploring the technical implementation, the paper addresses key challenges such as environmental adaptability, user interface design, and scalability [4][6][15]. Furthermore, a roadmap for future enhancements, including deep learning integration and cloud-based architectures [1][3][7][17], is proposed to ensure the system remains relevant and effective. This research contributes significantly to modernizing attendance management processes, offering an efficient, scalable, and user-friendly alternative to traditional methods.},
        keywords = {Automated Attendance, Facial Recognition, LBPH, Haar Cascade, Biometrics, Real-Time Monitoring, Error Analysis, Scalable Systems, Deep Learning, Cloud Integration.},
        month = {May},
        }