YAGOUBI AsmaaZIGH HanaaMERAD BOUDIA2026-09-172026-09-172026https://dspace.univ-temouchent.edu.dz/handle/123456789/7642Manual attendance tracking in higher education institutions remains time-consuming, error-prone, and susceptible to fraud. This thesis presents the development of ClassTrack DZ, an intelligent automated attendance system based on facial recognition technology. The study begins with a theoretical overview of artificial intelligence and deep learning, followed by a critical analysis of state-of-the-art detection and recognition algorithms. A rigorous experimental methodology was employed to reproduce reference benchmarks while evaluating a key innovation: the integration of the YOLOv8 architecture to replace conventional detectors (Viola-Jones, HOG, MTCNN). Hybrid pipelines were compared across two datasets using standardized metrics (accuracy, precision, recall, F1-score, and inference time). Results demonstrate that YOLOv8 significantly accelerates multi-face de- tection while maintaining optimal accuracy. The YOLOv8 + Dlib configuration emerged as the most suitable compromise for real-time deployment in lecture halls. The modu- lar Python-based implementation ensures scientific reproducibility and features a secure pedagogical interface compliant with ethical standards for biometric data. This work validates the operational viability of facial recognition for modernizing academic admin- istration and paves the way for future enhancements, including liveness detection and embedded platform deployment.frFacial RecognitionYOLOv8Automated Attendance ManagementDeep LearningDlibClassTrack DZReal-Time System.Thesis