Accelerated Fingerprint Enhancement: A GPU-Optimized Mixed Architecture
  Approach
André Brasil Vieira Wyzykowski Anil K. Jain
This document presents a preliminary approach to latent fingerprint
enhancement, fundamentally designed around a mixed Unet architecture. It
combines the capabilities of the Resnet-101 network and Unet encoder, aiming to
form a potentially powerful composite. This combination, enhanced with
attention mechanisms and forward skip connections, is intended to optimize the
enhancement of ridge and minutiae features in fingerprints. One innovative
element of this approach includes a novel Fingerprint Enhancement Gabor layer,
specifically designed for GPU computations. This illustrates how modern
computational resources might be harnessed to expedite enhancement. Given its
potential functionality as either a CNN or Transformer layer, this Gabor layer
could offer improved agility and processing speed to the system. However, it is
important to note that this approach is still in the early stages of
development and has not yet been fully validated through rigorous experiments.
As such, it may require additional time and testing to establish its robustness
and usability in the field of latent fingerprint enhancement. This includes
improvements in processing speed, enhancement adaptability with distinct latent
fingerprint types, and full validation in experimental approaches such as
open-set (identification 1:N) and open-set validation, fingerprint quality
evaluation, among others.