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@article{201746,
        author = {AKILA M M and ABINAYA G and EZHIL MIRZAH M R and SRI RANJANI S},
        title = {REAL-TIME VIOLENCE DETECTION AND FACE RECOGNITION SYSTEM USING MATLAB WITH SMS ALERT INTEGRATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6273-6277},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201746},
        abstract = {The rapid rise in public safety incidents necessitates intelligent, automated surveillance systems capable of real-time threat detection. This paper presents a Real-Time Violence Detection and Face Recognition System developed using MATLAB's GUIDE framework, combining Spatio-Temporal Interest Point (STIP) feature extraction, MobileNetV2 deep learning classification, and cascade-based face detection to identify violent behavior and unauthorized individuals from live webcam feeds. When violence is detected, the system immediately triggers an audio alert via text-to-speech synthesis and dispatches an SMS notification to security personnel through Twilio's cloud messaging API. The face recognition module authenticates individuals against a pre-built database using Edge Histogram Descriptors (EHD) and records entry logs automatically. Experimental results demonstrate reliable real-time performance with an average detection accuracy of over 85% on test sequences. The integrated dual-module system provides a comprehensive, cost-effective solution for smart surveillance in public spaces, educational institutions, and secure facilities. The system's modular architecture supports future extensions including Aadhaar integration, multi-camera support, and cloud-based alert routing.},
        keywords = {Violence Detection, Face Recognition, STIP, MobileNetV2, MATLAB GUIDE, Twilio SMS Alert, Edge Histogram Descriptor, Real-Time Surveillance, Deep Learning, Spatio-Temporal Interest Points},
        month = {May},
        }