End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Copyright © 2020 Deshpande, Malemath, Patil and Chaugule..

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the "Automated Deep Convolutional Neural Network (DCNN)" and "Fast Fourier Transform (FFT)" filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an "FFT Enhancement" algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed "Automated Latent Minutiae Extractor (ALME)". Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed "Frequency Enhanced Minutiae Matcher (FEMM)" algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:7

Enthalten in:

Frontiers in robotics and AI - 7(2020) vom: 22., Seite 594412

Sprache:

Englisch

Beteiligte Personen:

Deshpande, Uttam U [VerfasserIn]
Malemath, V S [VerfasserIn]
Patil, Shivanand M [VerfasserIn]
Chaugule, Sushma V [VerfasserIn]

Links:

Volltext

Themen:

AFIS
DCNN
FFT
FVC2004
Frequency enhanced map
Journal Article
NIST SD27

Anmerkungen:

Date Revised 29.01.2021

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/frobt.2020.594412

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM320631796