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Applied Sciences
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Biometric Identification Systems: Recent Advances and Future Directions
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Biometric Identification Systems: Recent Advances and Future Directions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 11387

Special Issue Editors

Prof. Dr. Carmen Sánchez Ávila

E-Mail Website
Guest Editor
Department of Applied Mathematics to ICT, ETSI Telecomunicación and CeDInt, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: biometric system; cryptography; machine learning; deep learning; mathematical processing of medical signals and images; neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Mariano López-García

E-Mail Website
Guest Editor
Electronic Engineering Department, Universitat Politecnica de Catalunya, 08800 Vilanova i la Geltrú, Spain
Interests: biometrics; side-channel attacks; FPGA; embedded systems; crypto-processors
Special Issues, Collections and Topics in MDPI journals
Dr. Enrique Cantó Navarro

E-Mail Website
Guest Editor
Department of Electronic Engineering, Electrical and Automatics, Universitat Rovira i Virgili, 43007 Tarragona, Spain
Interests: algorithm acceleration; biometrics coprocessors; FPGA; embedded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The concept of “Biometrics” has become extremely relevant in the security field, as this enables the individual to be identified by “who she/he is”, instead of by “what she/he has” or “what she/he knows”. Therefore, the biometric systems utilize physiological or behavioral characteristics to recognize and verify an individual. During the last few years, biometrics has been confirmed as a promising research field as it provides increasingly efficient and reliable solutions to recognize individuals. During the last decade, there have been several continuous improvements in accuracy, speed, affordability and cost-effectiveness of the biometric systems. Particularly, during the last few years, several innovations have been proposed to identify individuals using the latest technology and with special focus on security.  Specific trends are associated with using biometrics to speed up the identification process in mobile technology, using multimodal biometric authentication systems utilizing at least two different biometric factors to increase security, using the cloud to host the solutions providing biometric identification resources, and so on.

In this Special Issue, we seek research and case studies that show the recent advances of the biometric identification systems as well as the future directions of this field. Example topics include, but are not limited to, the following:

  • Security, privacy and challenges in biometric systems;
  • Smart government solutions related to biometric systems;
  • Emergent biometrics techniques;
  • Challenges and opportunities related to biometric systems;
  • Trends linking biometrics with business applications;
  • Innovations and best practices related to biometric applications;
  • Biometrics in health systems;
  • Artificial Intelligence in biometrics;
  • Biometrics based on blockchain;
  • Biometrics on mobiles devices;
  • Future of biometrics in relation with policy development;
  • Cloud-based biometric applications;
  • Biometric coprocessors and accelerators;
  • Biometrics and post-quantum systems.

Prof. Dr. Carmen Sánchez Ávila
Dr. Mariano López-García
Dr. Enrique Cantó Navarro
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biometric systems
  • security
  • information technology
  • crypto-biometric systems
  • personal identification
  • trends in recognition
  • potential applications
  • emerging biometrics
  • biometrics on FPGAs

Published Papers (5 papers)

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Research

22 pages, 3390 KiB  
Article
Applying a Deep Learning Neural Network to Gait-Based Pedestrian Automatic Detection and Recognition
by Chih-Lung Lin, Kuo-Chin Fan, Chin-Rong Lai, Hsu-Yung Cheng, Tsung-Pin Chen and Chao-Ming Hung
Appl. Sci. 2022, 12(9), 4326; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094326 - 25 Apr 2022
Cited by 1 | Viewed by 1207
Abstract
Gait recognition is a noncontact biometric procedure that determines the identity or health status of a person by analyzing his or her walking posture and habits, including skeletal and joint movements. The most remarkable feature of this method is the possibility of conducting [...] Read more.
Gait recognition is a noncontact biometric procedure that determines the identity or health status of a person by analyzing his or her walking posture and habits, including skeletal and joint movements. The most remarkable feature of this method is the possibility of conducting recognition without demanding much cooperation from participants. Therefore, this recognition technique has attracted much attention from scholars. Additionally, because of the rapid development of graphics processing unit technology, related hardware and computation performance, the applications of deep-learning technology are considerably enhanced. The objective of this study was to apply a deep neural network (DNN), which employs deep-learning technology, to achieve gait-based automatic pedestrian detection and recognition. In contrast to using wearable devices to precisely capture skeletal and joint movements, pedestrian color-image sequences were used as input in this study. Subsequently, a pretraining convolutional neural network (CNN) was employed to capture pedestrian location and extract pedestrian dense optical flow to serve as concrete low-level feature inputs. Then, a finely-tuned DNN based on the wide residual network was employed to extract high-level abstract features. In addition, to overcome the difficulty of obtaining local temporal features by using a 2D CNN, part of the 3D convolutional structure was introduced into the CNN. This design enabled use of limited memory to acquire more effective features and enhance the DNN performance. The experimental results show that the proposed method has exceptional performance for pedestrian detection and recognition. Full article
(This article belongs to the Special Issue Biometric Identification Systems: Recent Advances and Future Directions)
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16 pages, 2672 KiB  
Article
Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
by Paula López-González, Iluminada Baturone, Mercedes Hinojosa and Rosario Arjona
Appl. Sci. 2022, 12(7), 3522; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073522 - 30 Mar 2022
Cited by 2 | Viewed by 2453
Abstract
Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition [...] Read more.
Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high. Full article
(This article belongs to the Special Issue Biometric Identification Systems: Recent Advances and Future Directions)
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16 pages, 3138 KiB  
Article
Online Signature Verification Systems on a Low-Cost FPGA
by Enrique Cantó Navarro, Rafael Ramos Lara and Mariano López García
Appl. Sci. 2022, 12(1), 378; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010378 - 31 Dec 2021
Viewed by 1849
Abstract
This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced [...] Read more.
This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system. Full article
(This article belongs to the Special Issue Biometric Identification Systems: Recent Advances and Future Directions)
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24 pages, 2991 KiB  
Article
The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
by Anas Husseis, Judith Liu-Jimenez and Raul Sanchez-Reillo
Appl. Sci. 2021, 11(17), 7883; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177883 - 26 Aug 2021
Cited by 1 | Viewed by 2381
Abstract
Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint [...] Read more.
Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both. Full article
(This article belongs to the Special Issue Biometric Identification Systems: Recent Advances and Future Directions)
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20 pages, 759 KiB  
Article
BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
by Paloma Tirado-Martin and Raul Sanchez-Reillo
Appl. Sci. 2021, 11(13), 5880; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135880 - 24 Jun 2021
Cited by 10 | Viewed by 2121
Abstract
Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases [...] Read more.
Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise. Full article
(This article belongs to the Special Issue Biometric Identification Systems: Recent Advances and Future Directions)
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