This I-Corps project is based on the development of a new class of biometric technologies that securely identifies individuals without physical contact. Current biometric systems, such as fingerprint or facial recognition, often face difficulties in everyday settings due to changes in lighting, user appearance, and other environmental factors. These challenges result in errors and delays, creating security risks and inefficiencies in critical areas such as healthcare, finance, government, and infrastructure. This solution applies advanced machine learning techniques to improve the way biometric systems learn and recognize unique features, increasing accuracy, reliability, and scalability for broad use. The technology performs effectively in real-world environments, enabling fast and secure identity verification without requiring physical interaction. The solution addresses the growing problem of identity theft and unauthorized access, which impacts millions of individuals annually. By reducing these risks, the technology enhances public safety, protects sensitive data, and increases operational efficiency. The technology also lowers the need for manual identity checks, saving time and resources. The project advances national interests by fostering scientific progress in secure digital identification, supporting economic stability, and strengthening infrastructure essential to public welfare. This I-Corps project utilizes experiential learning coupled with a first-hand inv