Identification of Missing Person using CNN

Abstract

Our project aims to leverage Convolutional Neural Networks (CNNs) for the identification of missing persons. CNNs, a class of deep learning algorithms widely used in image recognition tasks, offer promising potential in automating and enhancing the identification process. The project aims to develop a robust system using CNN models to match unidentified individuals with missing person databases, improving identification accuracy and providing closure. The proposed approach demonstrates potential in assisting law enforcement agencies and missing persons organizations by providing a reliable and efficient means of cross-referencing images from various sources, such as surveillance footage, social media, and public records. Additionally, the flexibility of CNNs allows for the integration of other biometric markers, including fingerprints and voice recognition, to increase the accuracy and reliability of identifications. This research underscores the importance of using artificial intelligence and machine learning in social good applications, highlighting the potential for technology to play a transformative role in reuniting families and bringing closure to unresolved cases. Future work will focus on refining the model, enhancing privacy protections, and ensuring ethical use in real-world applications.