AI-SEEKER: Real-Time Missing Individual Tracking Using Deep Learning-Based Facial Recognition

Abstract

In India a countless number of children are reported missing every year. Among the missing child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for identifying the reported missing child from the photos of multitude of children available, with the help of face recognition. The public can upload photographs of suspicious child into a common portal with landmarks and remarks. The photo will be automatically compared with the registered photos of the missing child from the repository. Classification of the input child image is performed and photo with best match will be selected from the database of missing children. For this, a deep learning model is trained to correctly identify the missing child from the missing child image database provided, using the facial image uploaded by the public. The Convolutional Neural Network (CNN), a highly effective deep learning technique for image based applications is adopted here for face recognition. The increasing number of missing person cases worldwide has become a significant social and security concern, necessitating the development of efficient and intelligent identification systems. Traditional methods of locating missing individuals rely heavily on manual processes, public alerts, and delayed investigations, which often result in low recovery rates. To address these limitations, this paper presents AI-SEEKER, a deep learning-based facial recognition system designed for the real-time tracking and identification of missing individuals.The proposed system leverages advanced deep learning techniques to automatically detect, extract, and recognize facial features from images and video streams. A Convolutional Neural Network (CNN) is employed for robust feature extraction, enabling the system to learn complex facial representations under varying conditions such as illumination changes, pose variations, and occlusions. These extracted features are further processed using a classification model to accurately match detected faces with a centralized database of missing individuals. AI-SEEKER integrates real-time video processing capabilities, allowing it to analyze live surveillance feeds and identify potential matches instantly. The system is designed to operate efficiently in dynamic environments, making it suitable for deployment in public areas such as railway stations, airports, and urban surveillance networks. Additionally, the system incorporates a scalable database structure, enabling continuous updates and efficient retrieval of facial data. To enhance accuracy and performance, the system utilizes preprocessing techniques such as face alignment and normalization. Data augmentation methods are also applied during training to improve generalization and reduce overfitting. The model is evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score, demonstrating high reliability in identifying individuals across diverse datasets.One of the key contributions of this work is the integration of deep learning-based facial recognition with real-time processing, enabling faster response times compared to conventional systems. Furthermore, the system supports automated alerts, notifying authorities when a potential match is detected, thereby reducing the time required for intervention.The proposed system also considers ethical and privacy aspects by ensuring secure data handling and controlled access to sensitive information. Role-based access mechanisms are implemented to restrict unauthorized usage of the system.In conclusion, AI-SEEKER provides an effective and scalable solution for missing individual identification by combining deep learning, facial recognition, and real-time data processing. The system significantly improves the efficiency and accuracy of locating missing persons, contributing to enhanced public safety and law enforcement capabilities. Future work may focus on integrating multimodal biometric data and improving recognition performance in highly challenging environments.