Citation: Bakheet, S.; Alsubai, S.;
Alqahtani, A.; Binbusayyis, A. Robust
Fingerprint Minutiae Extraction and
Matching Based on Improved SIFT
Features. Appl. Sci. 2022, 12, 6122.
https://doi.org/10.3390/
app12126122
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and Manolis
Maragoudakis
Received: 27 April 2022
Accepted: 8 June 2022
Published: 16 June 2022
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Article
Robust Fingerprint Minutiae Extraction and Matching Based on
Improved SIFT Features
Samy Bakheet
1
, Shtwai Alsubai
2,
*, Abdullah Alqahtani
2
and Adel Binbusayyis
2
1
Faculty of Computers and Artificial Intelligence, Sohag University, Sohag 82524, Egypt;
samy.bakheet@fci.sohag.edu.eg
2
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University,
Al Kharj 11942, Saudi Arabia; aq.alqahtani@psau.edu.sa (A.A.); a.binbusayyis@psau.edu.sa (A.B.)
* Correspondence: sa.alsubai@psau.edu.sa
Abstract:
Minutiae feature extraction and matching are not only two crucial tasks for identifying
fingerprints, but also play an eminent role as core components of automated fingerprint recognition
(AFR) systems, which first focus primarily on the identification and description of the salient minutiae
points that impart individuality to each fingerprint and differentiate one fingerprint from another, and
then matching their relative placement in a candidate fingerprint and previously stored fingerprint
templates. In this paper, an automated minutiae extraction and matching framework is presented for
identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT)
detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization,
thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an
optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and
described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given
fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning
a score for each match based on the Euclidean distance between the SIFT descriptors of the two
matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can
potentially eliminate almost all the false matches, while discarding very few correct matches (less than
4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets
demonstrate that the proposed framework delivers comparable or superior performance to several
state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%.
Keywords: fingerprint minutiae; SIFT feature detection; feature matching; FVC2004 database; EER
1. Introduction
Biometrics is often identified as the science of recognizing an individual through his
physical/behavioral traits in addition to physiological characteristics. The characteristics
that can be used by biometric systems typically involve fingerprint recognition, facial
identification, voice recognition and handwriting recognition systems. Among all biometric
techniques, fingerprint recognition is the most widely used for personal identification
systems, due to its relative permanence and uniqueness [
1
]. Due to the relatively high
level of fingerprint accuracy among all the biometric traits, recent years have witnessed
a fairly substantial upswing in the use of many digital fingerprint reading devices in our
day-to-day lives. However, these modern devices are being used increasingly for a wide
variety of purposes, e.g., for the attendance of the staff before and after their work as a
login password in computers or the key to a locker, etc. Fingerprints are thought to be
excellent individualizing evidence because they are permanent from birth to death and
very unique for each individual (i.e., the probability of two fingerprints being the same is
64 billion to 1.2, according to mathematical assumptions). Furthermore, they are easy to
verify and leave marks on every object a person touches. This makes fingerprint-based
Appl. Sci. 2022, 12, 6122. https://doi.org/10.3390/app12126122 https://www.mdpi.com/journal/applsci
