Generative Convolutional Networks for Latent Fingerprint Reconstruction

05/04/2017
by   Jan Svoboda, et al.
0

Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.

READ FULL TEXT

page 1

page 6

page 7

11/29/2011

Minutiae Extraction from Fingerprint Images - a Review

Fingerprints are the oldest and most widely used form of biometric ident...
12/04/2009

Fingerprint Verification based on Gabor Filter Enhancement

Human fingerprints are reliable characteristics for personnel identifica...
06/26/2022

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

Latent fingerprint enhancement is an essential pre-processing step for l...
07/31/2018

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Performing recognition tasks using latent fingerprint samples is often c...
08/08/2012

Performance Measurement and Method Analysis (PMMA) for Fingerprint Reconstruction

Fingerprint reconstruction is one of the most well-known and publicized ...
08/03/2015

Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

With the growing use of biometric authentication systems in the past yea...
09/16/2020

Characteristic and Necessary Minutiae in Fingerprints

Fingerprints feature a ridge pattern with moderately varying ridge frequ...