A Logical Design of Internet of Things Assisted Missing People Detection Scheme using Human Computer Interaction Technology

The paper introduces a logical framework for a Missing People Detection Scheme that harnesses the capabilities of the Internet of Things (IoT) alongside Human-Computer Interaction (HCI) technology. By amalgamating data-driven methodologies, deep learning advancements, and facial recognition technology, the scheme endeavors to enhance the efficiency of identifying and resolving missing person cases. Commencing with the meticulous collection and refinement of extensive data concerning missing individuals, encompassing social context, disappearance timing, and location, the scheme ensures data accuracy and reliability for subsequent analysis. Deep learning algorithms, particularly supervised models, are then deployed to construct a predictive model for missing person identification. Through iterative parameter adjustments during training, the model discerns patterns and correlations within the data to minimize prediction errors. Subsequent evaluation using separate test data measures the model's generalization capability and effectiveness through performance metrics. Addressing the challenge of missing data, the scheme deliberately incorporates incomplete information to ensure the model's predictive capacity despite such limitations. Case registration initiates the identification process, where uploaded images and details of missing individuals are stored and processed in a database. This lays the groundwork for facial recognition tasks, employing facial encoding techniques to extract distinctive features from uploaded images. Comparison of encoded features with stored data in the database facilitates identifying potential matches between found individuals and reported missing persons. Furthermore, Siamese Networks augment the scheme's capabilities for facial image comparison, leveraging deep learning to assess similarity and aid in the identification process. By offering a comprehensive solution to the complexities of missing person identification, the proposed scheme aims to contribute significantly to resolving such cases and reuniting individuals with their families.