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Advanced Machine Learning Algorithms for Biometrics and Its Applications

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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 2021) | Viewed by 25732

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Special Issue Editors

Prof. Dr. Larbi Boubchir

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Guest Editor

LIASD Research Lab, Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Elhadj Benkhelifa

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Guest Editor

Cloud Computing and Applications Research Lab, School of Computing and Digital Technologies, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Interests: cloud computing; cybersecurity; software engineering; software defined systems; cloud forensics; IoT; data governance

Prof. Dr. Boubaker Daachi

E-Mail Website
Guest Editor

LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: robotics; soft computing; BCI; WSN; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Biometrics has become a burgeoning research area due to the industrial and government needs for recognition and security concerns. It has also become a center of focus for many applications, such as identity authentication and identification in civil and forensic fields. Recently, advanced machine learning has received a great deal of attention in solving difficult and complex problems related to biometric recognition and security, where conventional machine learning techniques have shown their limitations.

This Special Issue aims to solicit original research papers, as well as review articles focusing on biometrics and its applications based on advanced machine learning algorithms. We are inviting original research works covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the biometrics domain.

In addition, the authors of the papers which will be presented at the 4th International Workshop on “Recent Advances in Biometrics and its Applications” that we are organizing in conjunction with the 43rd International Conference on Telecommunications and Signal Processing (TSP) will be invited to submit an extended version of their papers to this Special Issue after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least a 50% extension of new results. There are no page limitations for this journal.

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

Biometrics-based authentication and identification;
Physiological and behavioral biometrics (e.g., finger, palm, face, eye, ear, iris, retina, vein, gait, handwriting, voice);
Biometric feature extraction and matching;
Signal, image, and video processing in biometrics;
Advanced pattern recognition in biometrics;
Machine learning and deep learning in biometrics;
Artificial intelligence in biometrics;
Fusion techniques in biometrics;
Soft biometrics;
Multimodal biometrics;
Security and privacy in biometrics;
Big data challenges in biometrics;
Embedded biometric systems;
Emerging biometrics;
Related applications.

Prof. Dr. Larbi Boubchir
Prof. Dr. Elhadj Benkhelifa
Prof. Dr. Boubaker Daachi
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

biometrics
machine learning
artificial intelligence
algorithm

Published Papers (7 papers)

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Research

24 pages, 4071 KiB

Open AccessArticle

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

by Yazhou Wang, Bing Li, Yan Zhang, Jiaxin Wu and Qianya Ma

Appl. Sci. 2021, 11(18), 8497; https://doi.org/10.3390/app11188497 - 13 Sep 2021

Cited by 7 | Viewed by 2570

Abstract

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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13 pages, 4015 KiB

Open AccessArticle

Improving the Performance of Frequently Used Korean Handwritten Character Verification Based on Artificial Intelligence through Multimodal Fusion

by Kyung Won Jin and Eui Chul Lee

Appl. Sci. 2021, 11(18), 8413; https://doi.org/10.3390/app11188413 - 10 Sep 2021

Viewed by 1447

Abstract

Handwriting verification is a biometric recognition field that identifies individuals’ unique characteristics contained in their handwriting. A single written character shows subtle differences depending on habits accumulated over time or the manner of writing. Based on this, it is often adopted in forensic investigations and as evidence in court. Existing handwriting verification is conducted by an expert, and is affected by the expert’s ability or subjectivity, causing different results to arise depending on the expert. Therefore, we propose a handwriting verification method that excludes human subjectivity and has objectivity. Using computer vision and artificial intelligence (AI), we derived results that excluded human subjectivity, and the judgment strength was expressed through a likelihood ratio. To improve the existing method’s accuracy, we performed a more accurate verification through multimodal use from the biometric field. Multimodal handwriting verification is conducted using up to four characters (not just one) because individual handwriting in each character is different. For learning, n-fold tests were conducted to maintain test objectivity, and the average performance of single character-based verification was 80.14% and the multimodal method averaged 88.96%. Here, we proposed the objectivity of handwriting verification through learning using AI, and show that performance improved through multimodal fusion. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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16 pages, 635 KiB

Open AccessArticle

Entropy-Based Time Window Features Extraction for Machine Learning to Predict Acute Kidney Injury in ICU

by Chun-Te Huang, Rong-Ching Chang, Yi-Lu Tsai, Kai-Chih Pai, Tsai-Jung Wang, Chia-Tien Hsu, Cheng-Hsu Chen, Chien-Chung Huang, Min-Shian Wang, Lun-Chi Chen, Ruey-Kai Sheu, Chieh-Liang Wu and Chun-Ming Lai

Appl. Sci. 2021, 11(14), 6364; https://doi.org/10.3390/app11146364 - 09 Jul 2021

Cited by 1 | Viewed by 2888

Abstract

Acute kidney injury (AKI) refers to rapid decline of kidney function and is manifested by decreasing urine output or abnormal blood test (elevated serum creatinine). Electronic health records (EHRs) is fundamental for clinicians and machine learning algorithms to predict the clinical outcome of patients in the Intensive Care Unit (ICU). Early prediction of AKI could automatically warn the clinicians to review the possible risk factors and act in advance to prevent it. However, the enormous amount of patient data usually consists of a relatively incomplete data set and is very challenging for supervised machine learning process. In this paper, we propose an entropy-based feature engineering framework for vital signs based on their frequency of records. In particular, we address the missing at random (MAR) and missing not at random (MNAR) types of missing data according to different clinical scenarios. Regarding its applicability, we applied it to establish a prediction model for future AKI in ICU patients using 4278 ICU admissions from a tertiary hospital. Our result shows that the proposed entropy-based features are feasible to be used in the AKI prediction model and its performance improves as the data availability increases. In addition, we study the performance of AKI prediction model by comparing different time gaps and feature windows with the proposed vital sign entropy features. This work could be used as a guidance for feature windows selection and missing data processing during the development of a prediction model in ICU. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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14 pages, 2687 KiB

Open AccessFeature PaperArticle

Multi-Descriptor Random Sampling for Patch-Based Face Recognition

by Ismahane Cheheb, Noor Al-Maadeed, Ahmed Bouridane, Azeddine Beghdadi and Richard Jiang

Appl. Sci. 2021, 11(14), 6303; https://doi.org/10.3390/app11146303 - 08 Jul 2021

Viewed by 2110

Abstract

While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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29 pages, 49128 KiB

Open AccessArticle

Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment

by Alexandros A. Lavdas, Nikos A. Salingaros and Ann Sussman

Appl. Sci. 2021, 11(13), 6197; https://doi.org/10.3390/app11136197 - 04 Jul 2021

Cited by 17 | Viewed by 7198

Abstract

Eye-tracking technology is a biometric tool that has found many commercial and research applications. The recent advent of affordable wearable sensors has considerably expanded the range of these possibilities to fields such as computer gaming, education, entertainment, health, neuromarketing, psychology, etc. The Visual Attention Software by 3M (3M-VAS) is an artificial intelligence application that was formulated using experimental data from eye-tracking. It can be used to predict viewer reactions to images, generating fixation point probability maps and fixation point sequence estimations, thus revealing pre-attentive processing of visual stimuli with a very high degree of accuracy. We have used 3M-VAS software in an innovative implementation to analyze images of different buildings, either in their original state or photographically manipulated, as well as various geometric patterns. The software not only reveals non-obvious fixation points, but also overall relative design coherence, a key element of Christopher Alexander’s theory of geometrical order. A more evenly distributed field of attention seen in some structures contrasts with other buildings being ignored, those showing instead unconnected points of splintered attention. Our findings are non-intuitive and surprising. We link these results to both Alexander’s theory and Neuroscience, identify potential pitfalls in the software’s use, and also suggest ways to avoid them. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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18 pages, 7325 KiB

Open AccessArticle

Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction

by Octavio Loyola-González, Emilio Francisco Ferreira Mehnert, Aythami Morales, Julian Fierrez, Miguel Angel Medina-Pérez and Raúl Monroy

Appl. Sci. 2021, 11(9), 4187; https://doi.org/10.3390/app11094187 - 04 May 2021

Cited by 6 | Viewed by 4485

Abstract

We study the impact of minutiae errors in the performance of latent fingerprint identification systems. We perform several experiments in which we remove ground-truth minutiae from latent fingerprints and evaluate the effects on matching score and rank-n identification using two different matchers and the popular NIST SD27 dataset. We observe how missing even one minutia from a fingerprint can have a significant negative impact on the identification performance. Our experimental results show that a fingerprint which has a top rank can be demoted to a bottom rank when two or more minutiae are missed. From our experimental results, we have noticed that some minutiae are more critical than others to correctly identify a latent fingerprint. Based on this finding, we have created a dataset to train several machine learning models trying to predict the impact of each minutia in the matching score of a fingerprint identification system. Finally, our best-trained model can successfully predict if a minutia will increase or decrease the matching score of a latent fingerprint. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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14 pages, 1313 KiB

Open AccessArticle

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

by Sherif Said, Abdullah S. Karar, Taha Beyrouthy, Samer Alkork and Amine Nait-ali

Appl. Sci. 2020, 10(19), 6960; https://doi.org/10.3390/app10196960 - 05 Oct 2020

Cited by 13 | Viewed by 2728

Abstract

Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification. Full article

(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)

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