Intelligent Child Safety System: Facial Recognition for Missing Child Identification with Real-time Email Alerts to Authorities

Abstract

Intelligent Child Safety System uses live video monitoring, face recognition, and automated email notifications for rapid child-abduction response. Ongoing feeds are monitored, and a constantly updating database has continuously comparesa number of the children with Dlib\'s CNN and HOG-based detection methods, combined with another model based on VGG\'s CNN model for great results in even low-light, obscured faces and varied angles of facial positions. An instant alert is sent by email to law enforcement agencies, child-care agencies, and relevant bodies with an accompanying captured face once the match is made, thus allowing quick action. Also, real-time alerts go to an administrative dashboard, allowing the monitoring of recognition results and adjusting the settings of the alerts. Strong measures for security and privacy, such as access controls to sensitive information, are in place to ensure ethical and secure operation. This automated alert and decision-making facilitate effective real-time monitoring and response while greatly reducing traditional reliance on search. Enhancements for further improvements in accuracy and adaptability include multimodal biometric authentication, voice recognition, and predictive analytics. Under very challenging conditions, the system has high reliability due to its 95.0% accuracy, 93.0% precision, 95.2% recall, and an F1 score of 94.1%. It ensures precise detection of actual cases of missing children by reducing the number of false positive values. It can thereby deliver quick responses reliably since high F1 scores signify an equilibrium between recall and precision. Equipped with facial identification powered through AI, live monitoring, and an automated decision-making mechanism, Intelligent Child Safety is an effective and scalable tool for working through the course of retrieval for missing children.

Introduction

Children are a vital asset to any nation, and their safety is a major concern, especially in India where many children go missing daily due to abduction, trafficking, and exploitation. Traditional methods for finding missing children, such as manual searches and public awareness campaigns, are often slow and ineffective. According to official data, over 111,000 children were reported missing in India by 2016, with nearly half still untraced.

To address this urgent issue, an Intelligent Child Safety System leveraging AI-based facial recognition has been proposed. This system uses technologies like OpenCV, Dlib, and TensorFlow to analyze faces in real-time from live CCTV feeds in crowded public places. When a match is found against a database of missing children, automatic email alerts are sent to authorities for swift action. The system adapts and improves over time through deep learning, handling challenges like changes in age, lighting, and occlusion, thus reducing false positives and enhancing accuracy.

The literature review highlights advances in deep learning and facial recognition technologies, discussing their strengths and limitations. While convolutional neural networks (CNNs) offer high accuracy, they require large datasets and significant computational power. Other methods like HOG and SIFT have robustness issues or are computationally expensive. Ethical and privacy concerns around AI surveillance and child safety are also discussed, emphasizing the need for regulatory oversight.

The proposed system architecture includes a web interface for uploading images and videos, a backend server processing these inputs with AI models, and a database of facial embeddings for comparison. Data preprocessing and augmentation improve system robustness under varying real-world conditions. The system performs real-time face detection and matching, and upon identification, it notifies authorities via email to enable immediate intervention.

Conclusion

The Intelligent Child Safety System is a beacon of hope in the latter\'s endeavors to protect missing children, employing facial recognition technology in convenient and loving ways. By real-time monitoring, the system aspires to give instant email alerts which in return provide us with the ability to utilize the platform to spot and respond to actions quickly and safely, thus raising the bar possibilities to get them back home safely. What makes it distinct is the concern for one\'s integrity and morals, built in a way to emphasize human beings—which adds to a superior reliable and adaptable facility in maintaining child security in outdated modern crowded places. Looking ahead, the potential becomes endless. You can use sharp facial recognition that draws on the newest shadowy technology such as Vision Transformers or GAN, which will allow a new state of the art to detect a child\'s face – while the child walks on, in the dark, even in the middle of a crowd. A combination of voice recognition or the gait of a child could act in some kind of redundancy, while the speed of this process could climb radically if Edge AI performs everything directly in the cameras and the elimination of cloud service latency. Various levels of data would be safeguarded and sealed securely by blockchain technology, while intelligence analytics, on the other hand, might assist discern in advance possible dangerous places and make sense of any trending child trafficking scenarios. Think of what the system would do while crossing the borders: linking with Interpol and smart cities, being a safety net that born not just to find but also to prevent future cases of missing children. With time and care, this technology could change the face of child safety, and families would have somewhere to rest easy.

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Copyright

Copyright © 2025 Sugunalatha Chitturi, Yuvaraj Katuri, Anjani Prabha Maganti, N. Gayathri, Venkat Karumuri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.