Real-Time Face Detection for Surveillance and Safety

Abstract- This proposed system provides a complete study of a face recognition system which operates from a travel agency platform and demonstrates its use in discovering missing persons. The face detection and recognition process by the system combines CNN and HOG techniques through sequential processing. The pre-trained deep models enable this proposed system to deliver an accuracy rate of 88% through reasonable processing needs. The system demonstrates strong potential to identify missing persons both on buses and at destinations managed by travel agencies while addressing essential requirements faced by the travel sector. Environmental factors strongly affect recognition accuracy because perfect conditions enable 95% success but reduced lighting decreases the rate to 70% accuracy.