Machine Learning for the Real World: Applications and Insights

Every year, thousands of people, including both children and adults, go missing for many different reasons, including behavioural and mental health, natural disasters, accidental separations, and human trafficking. Facial recognition has already demonstrated its usefulness across a variety of fields in both humanitarian relief and security and surveillance. The aim of this project was to create an automated system that would orchestrate the identification of missing persons, utilising face recognition with real-time recognition and detection, eliminating human error and establishing minimal time for a response. This study provides an applied system that uses computer vision, machine learning, and database management in law enforcement agencies, non-profits, and families. The work was completed at the Department of MCA, Acharya Institute of Technology, Bangalore, India, from July 2025 to September 2025, executing experiments with benchmark datasets, test datasets, and live camera feeds. For face detection, a Haar Cascade Classifier was applied, while Local Binary Pattern Histogram (LBPH) was utilised for recognition. In addition, compared experiments were implemented with YOLO (version 5) for real-time detection and HOG + SVM for feature-based recognition. Pre-processing (resizing, normalisation, and histogram equalisation) was done before feature extraction, and the images were stored in MongoDB for comparison against missing persons cases. Once a match was established for a missing person found, an established Email API facilitated automated alerts to authorities and registered family members. The conventional approach (Haar + LBPH) reached identification accuracy from 82–88% in good light and frontal angle, YOLO (v5) surpassed, showing slightly over 98%, while providing balanced precision/recall, HOG + SVM had similar results around an identical 96%. The system was capable of real-time performance on a CPU machine, making it great for low-cost and light deployment.

The proposed system shows automated alerts combining anthropometric (Haar, LBPH and HOG + SVM) methods and advanced deep learning (YOLO, CNN-based architectures (ResNet, ArcFace and FaceNet) to provide a reliable and scalable solution for missing persons identification. The system reduces human effort, increases response time velocity and opens up a platform for development in the area of deep learning, multimodal data fusion, and mobile-based applications.