Fingerprint Representation

Fingerprint representation research focuses on developing efficient and accurate methods for encoding fingerprint images into compact, robust digital representations suitable for biometric authentication and other applications. Current research emphasizes deep learning approaches, particularly convolutional neural networks and vision transformers, to extract fixed-length feature vectors capturing both global and local fingerprint characteristics, often incorporating techniques like style transfer and manifold learning to improve generalization and robustness. These advancements aim to improve the accuracy, speed, and security of fingerprint-based systems, impacting fields ranging from law enforcement to personal device security.