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Hybrid framework for identifying partial latent fingerprints using minutiae points and pores

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
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Abstract

Latent fingerprints play a pivotal role in the forensics for the investigation of the crimes. Mostly, latent fingerprints found at the crime scene are partial prints. Thus, fingerprint features required for identifying the latent fingerprint is not always available. Approaches for reconstructing the partial latent fingerprints to fill the missing area is highly probabilistic. As a result, numerous false features may be extracted during feature extraction, which can affect the identification accuracy. Thus, the paper aims to propose a hybrid framework for identifying partial latent fingerprints using minutiae points (level 2) and pores (level 3), which could increase the identification accuracy. Results are evaluated on the CSRC Latent Fingerprint Touch-less Database created using Reflected Ultra Violet Imaging System (RUVIS), which shows improvement in the Rank-k identification accuracy when similarity scores of both minutiae and pores are combined. Moreover, an analysis of the identification accuracy for the number of minutiae points available in the partial latent fingerprints shows that pores could help in identifying partial latent fingerprints that have less than 5 minutiae points.

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Abbreviations

CSRC:

Cyber Security Research Centre

RUVIS:

Reflected Ultra Violet Imaging System

AFIS:

Automatic Fingerprint Identification System

GAN:

Generative Adversarial Network

NIST SD27:

National Institute of Standards and Technology Special Database 27

ELFT-EFS-PC:

Evaluation of Latent Fingerprint Technologies—Extended Feature Sets—Public Challenge

WVU:

West Virginia University

IIIT-D:

Indraprastha Institute of Information Technology Delhi

SLF:

Simultaneous Latent Fingerprint

FVC:

Fingerprint Verification Competition

COTS:

Commercial Off-the-shelf

DRP:

Distinctive Ridge Point

CNN:

Convolutional Neural Network

EF:

Exemplar Fingerprints

PLF:

Partial Latent Fingerprints

PIL:

Python Imaging Library

FCN:

Fully Convolution Neural Network

SIFT:

Scale Invariant Feature Transform

PolyU HRF:

Polytechnic University High-Resolution-Fingerprint

CMC:

Cumulative Matching Curve

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Authors and Affiliations

  1. Cyber Security Research Centre, Punjab Engineering College (Deemed To Be University), Chandigarh, 160012, India

    Nancy Singla & Manvjeet Kaur

  2. Department of Computer Science and Engineering, Punjab Engineering College (Deemed To Be University), Chandigarh, 160012, India

    Sanjeev Sofat

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  1. Nancy Singla
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  2. Manvjeet Kaur
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Correspondence to Nancy Singla.

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Singla, N., Kaur, M. & Sofat, S. Hybrid framework for identifying partial latent fingerprints using minutiae points and pores. Multimed Tools Appl 81, 19525–19542 (2022). https://doi.org/10.1007/s11042-021-11541-4

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