Development of a real time facial emotion recognition system based on deep learning and CCTV integration for smart surveillance applications

The purpose of this research is to develop a real-time facial emotion recognition (FER) system that integrates deep learning techniques with closed-circuit television (CCTV) for intelligent surveillance applications. The system aims to overcome technical and environmental challenges often encountered in real-world CCTV environments, such as low image resolution, illumination instability, and varying facial orientations. The study adopts a systematic methodology that includes a literature review, research framework design, dataset preparation, model training using convolutional neural networks (CNNs), and web-based system implementation for real-time monitoring and alerting. The proposed model, developed on the TensorFlow platform and fine-tuned with FER2013, RAF-DB, and AffectNet datasets, achieved an overall accuracy of 80.24%, with precision, recall, and F1-score values of 80.59%, 79.54%, and 80.05%, respectively. The web application allows seamless integration with CCTV feeds, enabling real-time emotion detection, alert notifications via screen and email, and historical data analysis for behavioral trend evaluation. The findings indicate that the proposed FER system can be effectively incorporated into existing surveillance infrastructures to enhance situational awareness and proactive decision-making. This contributes to improving public safety in sensitive environments such as schools, public facilities, and government institutions. The practical implications suggest that the system provides a scalable framework for emotion-aware surveillance, which can be extended to multimodal emotion recognition and edge computing to improve responsiveness, privacy, and scalability in future research.