VanishVault

Abstract - The accelerating number of missing person cases presents a major social and executive challenge, especially in densely peopled regions. Being identification systems are largely homemade, fractured, and warrant real- time collaboration between families, authorities, and levy associations. This paper presents VanishVault, an artificial intelligence- grounded web platform designed to ameliorate missing person identification using facial recognition and centralized data operation. The system integrates deep literacy models for face embedding birth, similarity- grounded matching algorithms, and secure pall- grounded storehouse to enable automated comparison between reported missing individualities and unidentified persons. A structured case operation dashboard allows authorized users to cover progress and respond to match cautions efficiently. Experimental evaluation demonstrates dependable matching delicacy, optimized processing time, and scalable performance under concurrent operation conditions. The proposed frame highlights the practical operation of computer vision and intelligent data systems in philanthropic and public safety disciplines. Key Words: Facial Recognition, Missing Person Identification, Deep Learning, Computer Vision, Case Management, Artificial Intelligence.