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Advances in Biometrics: Sensors, Algorithms, and Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13409

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Special Issue Editors

Dr. Min Wang

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Guest Editor

School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: biometrics; pattern recognition; privacy and security; bio-cryptography
Special Issues, Collections and Topics in MDPI journals

Dr. Xuefei Yin

E-Mail Website
Guest Editor

School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: biometrics; privacy preserving; information forensic; IoT; cybersecurity

Dr. Yanming Zhu

E-Mail Website
Guest Editor

School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: biometrics; biomedical image computing; machine and deep learning; digital signal processing; privacy and security

Prof. Dr. Jiankun Hu

E-Mail Website
Guest Editor

School of Engineering and Information technology, University of New South Wales Canberra, Northcott Drive, Canberra, ACT 2610, Australia
Interests: biometrics; security; cybersecurity; bio-cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric technology continues to stride forward with the development of new sensing technologies. Its applications range from traditional access control and identity management to emerging scenarios such as IoT, human–computer interaction, and biometric cryptosystems. This led to the development of new biometric modalities based on physiological signals collected from brain, heart, wrist, and other body locations. Meanwhile, feature retrieval and recognition using deep learning has become a hot topic, and the fusion of multiple modalities as well as security and privacy issues are still open research questions to be addressed.

This Special Issue focuses on recent advances in biometrics, including new sensing technologies and biometric modalities, feature retrieval and recognition algorithms, multi-modality fusion, the security and privacy mechanisms, and novel applications of biometric systems in various scenarios such as IoT and bio-cryptosystems. We welcome studies on traditional biometrics based on 2D fingerprint, face, hand geometry, iris, voice, gait, signature, as well as emerging biometrics using 3D fingerprint, 3D face, brain signals, heart signals, and new physical, physiological, and behavioral characteristics. New sensing technologies and security mechanisms that expand and enhance biometric systems are also welcome.

Topics of interest include, but are not limited to, the following methods:

Sensing technologies for biometrics;
Machine learning for biometrics;
Biometric feature retrieval and recognition;
Multi-modal fusion;
Security mechanisms for biometric systems;
Anti-spoof technologies in biometrics;
Innovative applications of biometrics;
Bio-cryptographic systems;
Biometrics in IoT applications.

Dr. Min Wang
Dr. Xuefei Yin
Dr. Yanming Zhu
Prof. Dr. Jiankun Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

Topics of interest include, but are not limited to, the following methods:

Sensing technologies for biometrics;
Machine learning for biometrics;
Biometric feature retrieval and recognition;
Multi-modal fusion;
Security mechanisms for biometric systems;
Anti-spoof technologies in biometrics;
Innovative applications of biometrics;
Bio-cryptographic systems;
Biometrics in IoT applications.

Published Papers (6 papers)

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Research

28 pages, 1432 KiB

Open AccessArticle

Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges

by Akmal Jahan Mohamed Abdul Cader, Jasmine Banks and Vinod Chandran

Sensors 2023, 23(14), 6591; https://doi.org/10.3390/s23146591 - 21 Jul 2023

Cited by 2 | Viewed by 3449

Abstract

The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric system’s accuracy and performance. Imaging modality, such as contact and contactless, plays a key role in poor image quality, and therefore, paying attention to imaging modality is important to obtain better performance. Further, underlying physical principles and the working of the sensor can lead to their own forms of distortions during acquisition. There are certain challenges in each module of the fingerprint recognition pipeline, particularly sensors, image acquisition and feature representation. Present reviews in fingerprint systems only analyze the imaging techniques in fingerprint sensing that have existed for a decade. However, the latest emerging trends and recent advances in fingerprint sensing, image acquisition and their challenges have been left behind. Since the present reviews are either obsolete or restricted to a particular subset of the fingerprint systems, this work comprehensively analyzes the state of the art in the field of contact-based, contactless 2D and 3D fingerprint systems and their challenges in the aspects of sensors, image acquisition and interoperability. It outlines the open issues and challenges encountered in fingerprint systems, such as fingerprint performance, environmental factors, acceptability and interoperability, and alternate directions are proposed for a better fingerprint system. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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28 pages, 11151 KiB

Open AccessArticle

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

by Muhammad Sheharyar Asif, Muhammad Shahzad Faisal, Muhammad Najam Dar, Monia Hamdi, Hela Elmannai, Atif Rizwan and Muhammad Abbas

Sensors 2023, 23(10), 4635; https://doi.org/10.3390/s23104635 - 10 May 2023

Cited by 1 | Viewed by 2508

Abstract

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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18 pages, 16830 KiB

Open AccessArticle

Yttrium-Iron Garnet Magnetometer in MEG: Advance towards Multi-Channel Arrays

by Ekaterina Skidchenko, Anna Butorina, Maxim Ostras, Petr Vetoshko, Alexey Kuzmichev, Nikolay Yavich, Mikhail Malovichko and Nikolay Koshev

Sensors 2023, 23(9), 4256; https://doi.org/10.3390/s23094256 - 25 Apr 2023

Cited by 3 | Viewed by 1748

Abstract

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of yttrium-iron garnet magnetometers (YIGMs) was demonstrated in an alpha-rhythm registration experiment. In this paper, we propose the analysis of lead-field matrices for different possible multi-channel on-scalp sensor layouts using YIGMs with respect to information theory. Real noise levels of the new sensor were used to compute signal-to-noise ratio (SNR) and total information capacity (TiC), and compared with corresponding metrics that can be obtained with well-established MEG systems based on superconducting quantum interference devices (SQUIDs) and optically pumped magnetometers (OPMs). The results showed that due to YIGMs’ proximity to the subject’s scalp, they outperform SQUIDs and OPMs at their respective noise levels in terms of SNR and TiC. However, the current noise levels of YIGM sensors are unfortunately insufficient for constructing a multichannel YIG-MEG system. This simulation study provides insight into the direction for further development of YIGM sensors to create a multi-channel MEG system, namely, by decreasing the noise levels of sensors. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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16 pages, 517 KiB

Open AccessArticle

Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals

by Vangelis P. Oikonomou

Sensors 2023, 23(5), 2425; https://doi.org/10.3390/s23052425 - 22 Feb 2023

Cited by 2 | Viewed by 1315

Abstract

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain’s responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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19 pages, 4016 KiB

Open AccessArticle

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space

by Suixian Li, Kaida Xiao and Pingqi Li

Sensors 2023, 23(2), 810; https://doi.org/10.3390/s23020810 - 10 Jan 2023

Cited by 3 | Viewed by 1992

Abstract

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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22 pages, 4423 KiB

Open AccessArticle

Can Microsaccades Be Used for Biometrics?

by Kiril Alexiev and Teodor Vakarelski

Sensors 2023, 23(1), 89; https://doi.org/10.3390/s23010089 - 22 Dec 2022

Cited by 1 | Viewed by 1368

Abstract

Human eyes are in constant motion. Even when we fix our gaze on a certain point, our eyes continue to move. When looking at a point, scientists have distinguished three different fixational eye movements (FEM)—microsaccades, drift and tremor. The main goal of this paper is to investigate one of these FEMs—microsaccades—as a source of information for biometric analysis. The paper argues why microsaccades are preferred for biometric analysis over the other two fixational eye movements. The process of microsaccades’ extraction is described. Thirteen parameters are defined for microsaccade analysis, and their derivation is given. A gradient algorithm was used to solve the biometric problem. An assessment of the weights of the different pairs of parameters in solving the biometric task was made. Full article

(This article belongs to the Special Issue Advances in Biometrics: Sensors, Algorithms, and Systems)

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