International Journal of Science, Engineering and TechnologyAuthors: Shrijana kumari Dixit, Ritik Sinha, Pratuish Anand, Saumya, Professor Dr. Md Iqbal
Abstract: Conventional attendance management systems rely heavily on manual recording, RFID-based identification, or contact-dependent biometric methods, which often suffer from issues including proxy attendance, operational delays, scalability limitations, and reduced analytical capabilities. To address these challenges, this research presents FaceAIDetect, an intelligent face recognition and emotion-aware attendance management framework that integrates Computer Vision, Artificial Intelligence, and Machine Learning for automated identity verification and attendance generation. The proposed system performs real-time facial acquisition, preprocessing, face detection, feature extraction, embedding generation, similarity-based matching, emotion recognition, and automated attendance recording within a unified architecture. Facial representations are generated through encoding mechanisms and indexed using similarity search techniques to improve recognition speed and scalability. The framework extends traditional attendance systems by incorporating emotional state analysis, enabling contextual understanding of user behavior during attendance operations. The implementation utilizes Python-based technologies including OpenCV, FAISS, MongoDB, and modern machine learning pipelines for recognition and storage management. Experimental evaluation demonstrates improvements in operational efficiency, reduced manual intervention, enhanced attendance authenticity, and effective real-time performance under controlled deployment conditions. The proposed system contributes toward the development of intelligent and contactless attendance infrastructures suitable for educational institutions, workplaces, and secure monitoring environments. Future enhancements may include cloud deployment, federated learning integration, mobile accessibility, and advanced behavioral analytics.
