Deep Learning Approaches for Fingerprint Verification




Dalvi, Nikita
Pham, Van Vung

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Institute for Homeland Security


Fingerprint verification is vital because it provides a unique and permanent way to identify individuals. This technology is widely used in various areas like law enforcement, access control, and identity verification processes. Existing approaches for fingerprint verification tasks suffer from low accuracy due to training directly on low-quality and latent fingerprints. Therefore, this work proposes to utilize recent advancements in deep learning and computer vision to (1) enhance fingerprint image quality; (2) extract and verify that the minutiae are retained after enhancement; and (3) perform fingerprint verification tasks. Specifically, this work experiments with (1) Super-Resolution Convolutional Neural Network (SRCNN), Fast SRCNN, and Very Deep Super Resolution (VDSR) for fingerprint image enhancement; (2) Finger-Flow for minutia extraction; and (3) Siamese neural network for fingerprint verification. The experiment results indicate that among the experimented super resolution approaches, VDSR outperforms the others. Additionally, it can retain minutiae in the enhanced version and shows great potential to enhance latent fingerprints, which are less visible. Most importantly, the verification performances improve on the enhanced fingerprints versus low-resolution counterparts.



super-resolution convolutional neural networks, minutiae extraction, fingerprint verification, deep learning


Dalvi, N. & Pham, V. (2023) Deep Learning Approaches for Fingerprint Verification Classification Using Grayscale Images with Deep Learning. (Report No. IHS/CR-2023-1005). The Sam Houston State University Institute for Homeland Security.