3D Fingerprint Fragment Reconstruction of Condensed Microdroplet Clusters Driven by Directed Mass Transfer

Fingerprint recognition technology plays a critical role in modern security and information protection. Traditional 2D fingerprint recognition methods remain limited due to an imbalance between growing security demands and inefficiency of encoding detailed information. While various 3D fingerprint technologies have been introduced recently, their practical application is restricted by complex sampling procedures and bulky equipment. This paper proposes a new 3D fingerprint fragments reconstruction method based on the condensation of microdroplet clusters, resulting in efficiently extracting detailed structural information from fingerprint patterns. By identifying the distinctive topology features of fingerprint valleys, a micrometer-scale vapor transport model is developed to capture the condensation process. A differential approach is used to divide the microdroplet clusters, which are formed when a finger is pressed against a cold surface, into discrete units. In each unit, the diffusion distance and mass transfer during the condensation process are calculated. Nonlinear regression techniques are then applied to reconstruct the 3D fingerprint fragments. Furthermore, the experimental validation shows excellent consistency with premeasured fingerprint data, exhibiting a reconstruction error of less than 9.3%. It offers a significant improvement in capturing high-density fingerprint data within a short time frame, completing the data acquisition in less than 1 second. This method significantly reduces acquisition time compared to ultrasound imaging techniques, which typically involve complex procedures. Additionally, it offers a more efficient alternative to deep learning methods, which require extensive data training and computational processes. This 3D fingerprint reconstruction method provides an efficient, low-cost and easy to operate solution. It holds the potential to significantly enhance personal identification and information protection systems, contributing to the advancement of 3D fingerprint recognition technology in practical applications.