A Review On Integrated Facial Attendance And Sentiment Tracking Systems Using Expression Recognition

Traditional attendance monitoring systems rely heavily on manual processes or contact-based biometric solutions, which often lead to inefficiencies, proxy attendance, and lack of real-time behavioural insights 7. Recent advancements in computer vision 5 have introduced facial recognition-based attendance systems; however, most existing solutions focus only on identity verification and fail to analyze participant engagement or emotional response during sessions 6. This paper presents a comprehensive review and analysis of an integrated Facial Attendance and Sentiment Tracking System (FASTER), which combines real-time face detection 1, facial recognition using LBPH 2 and SVM classifiers 3, and expression-based sentiment monitoring 6 within a lightweight client-server architecture. Unlike previous systems that utilize either attendance automation or emotion detection independently, the proposed approach integrates both functionalities using OpenCV-based face detection 8, machine learning classifiers, and real-time data logging mechanisms. The system emphasizes low computational overhead, offline ca- pability, and user-friendly GUI-based interaction, making it suit- able for educational and organizational environments. Through comparative analysis with existing research, this study identifies key limitations in prior work and highlights the novelty of a unified attendance and sentiment-aware monitoring framework.