André Brasil Vieira Wyzykowski

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.

André Brasil Vieira Wyzykowski, Mauricio Pamplona Segundo, Rubisley de Paula Lemes

Today's legal restrictions that protect the privacy of biometric data are hampering fingerprint recognition researches. For instance, all high-resolution fingerprint databases ceased to be publicly available. To address this problem, we present a novel hybrid approach to synthesize realistic, high-resolution fingerprints. First, we improved Anguli, a handcrafted fingerprint generator, to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a CycleGAN to transform these maps into realistic fingerprints. Unlike other CNN-based works, we can generate several images for the same identity. We used our approach to create a synthetic database with 7400 images in an attempt to propel further studies in this field without raising legal issues. We included sweat pore annotations in 740 images to encourage research developments in pore detection. In our experiments, we employed two fingerprint matching approaches to confirm that real and synthetic databases have similar performance. We conducted a human perception analysis where sixty volunteers could hardly differ between real and synthesized fingerprints. Given that we also favorably compare our results with the most advanced works in the literature, our experimentation suggests that our approach is the new state-of-the-art.