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Applied Sciences
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Application of Biometrics Technology in Security
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Application of Biometrics Technology in Security

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14521

Special Issue Editors

Prof. Dr. Julian Fierrez

E-Mail Website
Guest Editor
School of Engineering, University Autonoma de Madrid, 28049 Madrid, Spain
Interests: signal and image processing; AI fundamentals and applications; HCI; forensics; biometric systems for person identification and human behavior monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Gian Luca Marcialis

E-Mail Website
Guest Editor
Associate Professor, Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
Interests: pattern recognition; computer vision; biometrics; digital forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue on Applications of Biometrics Technology in Security for the MDPI journal Applied Sciences.

Biometrics technology refers to methods and systems to measure and make use of human signals and images for various purposes including human behavior understanding and identification.

In this Special Issue, we invite submission in the particular area of security applications of biometrics, including identification and authentication applications based on established (face, fingerprint, iris, handwritten signature, etc.) and novel biometrics (touch interaction with screens, keystroking, etc.). Novel contributions and advances in the security aspects of biometric applications are expected, including (but not limited to):

  • Security and privacy in biometric systems;
  • Biometrics in the cloud;
  • Distributed biometrics, including biometrics in Distributed Ledger systems;
  • Federated learning in biometric systems;
  • Attacks and countermeasures against biometric systems, including Presentation Attack Detection;
  • Robust biometric systems intrinsically robust against potential attacks;
  • Measuring and evaluating the security and privacy of biometric systems;
  • Explainability in biometric systems, including human-readable explanations of the decision-making processes and vulnerabilities of biometric systems.

Prof. Dr. Julian Fierrez
Dr. Gian Luca Marcialis
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.

Published Papers (7 papers)

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Research

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16 pages, 3352 KiB  
Article
Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
by Vasu Krishna Suravarapu and Hemprasad Yashwant Patil
Appl. Sci. 2023, 13(5), 3116; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053116 - 28 Feb 2023
Cited by 1 | Viewed by 1545
Abstract
Many biometrics advancements have been widely used for security applications. This field’s evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various [...] Read more.
Many biometrics advancements have been widely used for security applications. This field’s evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various state-of-the-art results (performance metrics) with the help of deep-learning-based architectures. The latest transformation has happened with the invention of transformers, which are used in NLP (natural language processing) and are presently being adapted for computer vision. In this work, we have implemented five different ViT- (vision transformer) based architectures for person identification and gender classification. The experiment was performed on the ViT architectures and their modified counterparts. In general, the samples selected for train:val:test splits are random, and the trained model may get affected by overfitting. To overcome this, we have performed 5-fold cross-validation-based analysis. The experiment’s performance matrix indicates that the proposed method achieved better results for gender classification as well as person identification. We also experimented with train-val-test partitions for benchmarking with existing architectures and observed significant improvements. We utilized the publicly available UBIPr dataset for performing this experimentation. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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14 pages, 3195 KiB  
Article
Coupling RetinaFace and Depth Information to Filter False Positives
by Loris Nanni, Sheryl Brahnam, Alessandra Lumini and Andrea Loreggia
Appl. Sci. 2023, 13(5), 2987; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052987 - 25 Feb 2023
Cited by 1 | Viewed by 1955
Abstract
Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and [...] Read more.
Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace or YOLOv7, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The method proposed by the authors is tested on a dataset of 549 images, which includes 614 upright frontal faces. The outcomes of the evaluation demonstrate that the method effectively minimizes false positives without compromising the overall detection rate. These findings suggest that incorporating depth information can enhance the accuracy of face detection. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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20 pages, 2499 KiB  
Article
Monocular Facial Presentation–Attack–Detection: Classifying Near-Infrared Reflectance Patterns
by Ali Hassani, Jon Diedrich and Hafiz Malik
Appl. Sci. 2023, 13(3), 1987; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031987 - 03 Feb 2023
Cited by 2 | Viewed by 1490
Abstract
This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is [...] Read more.
This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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19 pages, 2207 KiB  
Article
Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
by Rosario Arjona, Paula López-González, Roberto Román and Iluminada Baturone
Appl. Sci. 2023, 13(2), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/app13020757 - 05 Jan 2023
Viewed by 2386
Abstract
Homomorphic encryption is a powerful mechanism that allows sensitive data, such as biometric data, to be compared in a protected way, revealing only the comparison result when the private key is known. This is very useful for non-device-centric authentication architectures with clients that [...] Read more.
Homomorphic encryption is a powerful mechanism that allows sensitive data, such as biometric data, to be compared in a protected way, revealing only the comparison result when the private key is known. This is very useful for non-device-centric authentication architectures with clients that provide protected data and external servers that authenticate them. While many reported solutions do not follow standards and are not resistant to quantum computer attacks, this work proposes a secure biometric authentication scheme that applies homomorphic encryption based on the Classic McEliece public-key encryption algorithm, which is a round 4 candidate of the NIST post-quantum standardization process. The scheme applies specific steps to transform the features extracted from biometric samples. Its use is proposed in a non-device-centric biometric authentication architecture that ensures user privacy. Irreversibility, revocability and unlinkability are satisfied and the scheme is robust to stolen-device, False-Acceptance Rate (FAR) and similarity-based attacks as well as to honest-but-curious servers. In addition to the security achieved by the McEliece system, which remains stable over 40 years of attacks, the proposal allows for very reduced storage and communication overheads as well as low computational cost. A practical implementation of a non-device-centric facial authentication system is illustrated based on the generation and comparison of protected FaceNet embeddings. Experimental results with public databases show that the proposed scheme improves the accuracy and the False-Acceptance Rate of the unprotected scheme, maintaining the False-Rejection Rate, allows real-time execution in clients and servers for Classic McEliece security parameter sets of 128 and 256 bits (mceliece348864 and mceliece6688128, respectively), and reduces storage requirements in more than 90.5% compared to the most reduced-size homomorphic encryption-based schemes with post-quantum security reported in the literature. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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15 pages, 1647 KiB  
Article
Late Deep Fusion of Color Spaces to Enhance Finger Photo Presentation Attack Detection in Smartphones
by Emanuela Marasco and Anudeep Vurity
Appl. Sci. 2022, 12(22), 11409; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211409 - 10 Nov 2022
Cited by 3 | Viewed by 1134
Abstract
Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image [...] Read more.
Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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21 pages, 17024 KiB  
Article
Analysis of Score-Level Fusion Rules for Deepfake Detection
by Sara Concas, Simone Maurizio La Cava, Giulia Orrù, Carlo Cuccu, Jie Gao, Xiaoyi Feng, Gian Luca Marcialis and Fabio Roli
Appl. Sci. 2022, 12(15), 7365; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157365 - 22 Jul 2022
Cited by 5 | Viewed by 1897
Abstract
Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and [...] Read more.
Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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Review

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14 pages, 1342 KiB  
Review
A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
by Muhammad Hamza, Samabia Tehsin, Mamoona Humayun, Maram Fahaad Almufareh and Majed Alfayad
Appl. Sci. 2022, 12(24), 12545; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412545 - 07 Dec 2022
Cited by 9 | Viewed by 2775
Abstract
A robust facial recognition system that has soundness and completeness is essential for authorized control access to lawful resources. Due to the availability of modern image manipulation technology, the current facial recognition systems are vulnerable to different biometric attacks. Image morphing attack is [...] Read more.
A robust facial recognition system that has soundness and completeness is essential for authorized control access to lawful resources. Due to the availability of modern image manipulation technology, the current facial recognition systems are vulnerable to different biometric attacks. Image morphing attack is one of these attacks. This paper compares and analyzes state-of-the-art morphing attack detection (MAD) methods. The performance of different MAD methods is also compared on a wide range of source image databases. Moreover, it also describes the morph image generation techniques along with the limitations, strengths, and drawbacks of each morphing technique. Results are investigated and compared with in-depth analysis providing insight into the vulnerabilities of existing systems. This paper provides vital information that is essential for building a next generation morph attack detection system. Full article
(This article belongs to the Special Issue Application of Biometrics Technology in Security)
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