Special Issue "Recent Advances in Biometrics and its Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Assoc. Prof. Dr. Larbi Boubchir
Website
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues and Collections in MDPI journals
Prof. Dr. Boubaker Daachi
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 and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric recognition has become a burgeoning research area due to the industrial and government needs for security and privacy concerns. It has also become a center of focus for many authentication and identification applications in the civil and forensic fields. This Special Issue aims to solicit original research papers, as well as review articles focusing on recent advances in biometrics and its applications. We are inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in biometrics.

In addition, the authors of the papers which will be presented at the 3rd International Workshop on "Recent Advances in Biometrics and its Applications" that we are organizing within the framework of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) are invited to submit their extended versions to this Special Issue of the Journal Electronics 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, gait, handwriting, voice, etc.);
  • Biometric feature extraction and matching;
  • Signal, image, and video processing in biometrics;
  • Advanced pattern recognition in biometrics;
  • Machine learning and deep learning in biometrics;
  • Fusion techniques in biometrics;
  • Soft biometrics;
  • Multimodal biometrics;
  • Security and privacy in biometrics;
  • Big data challenges in biometrics;
  • Online biometric systems;
  • Embedded biometric systems;
  • Emerging biometrics;
  • Related applications

Assoc. Prof. Dr. Larbi Boubchir
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 papers will be 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. Electronics is an international peer-reviewed open access monthly 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 1400 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 recognition
  • Biometrics security and privacy
  • Pattern recognition
  • Signal, image, and video processing
  • Machine learning
  • Artificial intelligence

Published Papers (12 papers)

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Research

Open AccessFeature PaperArticle
Face–Iris Multimodal Biometric Identification System
Electronics 2020, 9(1), 85; https://doi.org/10.3390/electronics9010085 - 01 Jan 2020
Abstract
Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness [...] Read more.
Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Biometrics Using Electroencephalograms Stimulated by Personal Ultrasound and Multidimensional Nonlinear Features
Electronics 2020, 9(1), 24; https://doi.org/10.3390/electronics9010024 - 25 Dec 2019
Abstract
Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have been a focus. In this paper, brain waves (electroencephalograms [...] Read more.
Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have been a focus. In this paper, brain waves (electroencephalograms (EEGs)) were measured from ten experiment subjects. Individual features were extracted from the log power spectra of the EEGs using principal component analysis, and verification was achieved using a support vector machine. It was found that, for the proposed authentication method, the equal error rate (EER) for a single electrode was about 22–32%, and that, for a multiple electrodes, was 4.4% by using the majority decision rule. Furthermore, nonlinear features based on chaos analysis were introduced for feature extraction and then extended to multidimensional ones. By fusing the results of all electrodes when using the proposed multidimensional nonlinear features and the spectral feature, an EER of 0% was achieved. As a result, it was confirmed that individuals can be authenticated using induced brain waves when they are subjected to ultrasounds. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Experimental Validation of a Reliable Palmprint Recognition System Based on 2D Ultrasound Images
Electronics 2019, 8(12), 1393; https://doi.org/10.3390/electronics8121393 - 22 Nov 2019
Abstract
Ultrasound has been trialed in biometric recognition systems for many years, and at present different types of ultrasound fingerprint readers are being produced and integrated in portable devices. An important merit of the ultrasound is its ability to image the internal structure of [...] Read more.
Ultrasound has been trialed in biometric recognition systems for many years, and at present different types of ultrasound fingerprint readers are being produced and integrated in portable devices. An important merit of the ultrasound is its ability to image the internal structure of the hand, which can guarantee improved recognition rates and resistance to spoofing attacks. In addition, ambient noise like changes of illumination, humidity, or temperature, as well as oil or ink stains on the skin do not affect the ultrasound image. In this work, a palmprint recognition system based on ultrasound images is proposed and experimentally validated. The system uses a gel pad to obtain acoustic coupling between the ultrasound probe and the user’s hand. The collected volumetric image is processed to extract 2D palmprints at various under-skin depths. Features are extracted from one of these 2D palmprints using a line-based procedure. Recognition performances of the proposed system were evaluated by performing both verification and identification experiments on a home-made database containing 281 samples collected from 32 different volunteers. An equal error rate of 0.38% and an identification rate of 100% were achieved. These results are very satisfactory, even if obtained with a relatively small database. A discussion on the causes of bad acquisitions is also presented, and a possible solution to further optimize the acquisition system is suggested. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Auditory Perception Based Anti-Spoofing System for Human Age Verification
Electronics 2019, 8(11), 1313; https://doi.org/10.3390/electronics8111313 - 08 Nov 2019
Cited by 2
Abstract
Biometric systems are considered an efficient component for identification in the developing modern technologies. The aim of biometric systems is to verify or determine the identity of a user through his/her biological and behavioral characteristics. The threat of spoof attacks is always an [...] Read more.
Biometric systems are considered an efficient component for identification in the developing modern technologies. The aim of biometric systems is to verify or determine the identity of a user through his/her biological and behavioral characteristics. The threat of spoof attacks is always an important issue in biometric verification and authentication, which requires an updated and stronger protection system. In this article, we propose an anti-spoofing system based on auditory perception responses. To the best of our knowledge, this is the first time that an auditory perception based anti-spoofing system has been presented for age verification. The proposed auditory perception based anti-spoofing system was evaluated with 770 trials conducted by many subjects of each gender and age range (12–65 years of age). The results achieved are encouraging, as the auditory perception based system showed the lowest Equal Error Rate (EER) value of 5.5%. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Impact of Aging on Three-Dimensional Facial Verification
Electronics 2019, 8(10), 1170; https://doi.org/10.3390/electronics8101170 - 15 Oct 2019
Abstract
Age progression is associated with poor performance of verification systems. Thus, there is a need for further research to overcome this problem. Three-dimensional facial aging modeling for employment in verification systems is highly serviceable, and able to acknowledge how variations in depth and [...] Read more.
Age progression is associated with poor performance of verification systems. Thus, there is a need for further research to overcome this problem. Three-dimensional facial aging modeling for employment in verification systems is highly serviceable, and able to acknowledge how variations in depth and pose can provide additional information to accurately represent faces. In this article, the impact of aging on the performance of three-dimensional facial verification is studied. For this purpose, we employed three-dimensional (3D) faces obtained from a 3D morphable face aging model (3D F-FAM). The proposed 3D F-FAM was able to simulate the facial appearance of a young adult in the future. A performance evaluation was completed based on three metrics: structural texture quality, mesh geometric distortion and morphometric landmark distances. The collection of 500 textured meshes from 145 subjects, which were used to construct our own database called FaceTim V.2.0, was applied in performance evaluation. The experimental results demonstrated that the proposed model produced satisfying results and could be applicable in 3D facial verification systems. Furthermore, the verification rates proved that the 3D faces achieved from the proposed model enhanced the performance of the 3D verification process. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network
Electronics 2019, 8(10), 1109; https://doi.org/10.3390/electronics8101109 - 01 Oct 2019
Cited by 2
Abstract
As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional [...] Read more.
As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
Electronics 2019, 8(9), 975; https://doi.org/10.3390/electronics8090975 - 31 Aug 2019
Abstract
Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the [...] Read more.
Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR). Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessFeature PaperArticle
Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks
Electronics 2019, 8(8), 907; https://doi.org/10.3390/electronics8080907 - 17 Aug 2019
Cited by 5
Abstract
Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) [...] Read more.
Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Customized Vibration Generator for State of Health Monitoring of Prosthetic Implants and Pseudo-Bionic Machine–Human Feedbacks
Electronics 2019, 8(7), 810; https://doi.org/10.3390/electronics8070810 - 19 Jul 2019
Abstract
Modern industrial, household and other equipment include sophisticated power mechanisms and complicated control solutions that require tighter human–machine–human interactions to form the structures known as cyber–physical–human systems. Their significant parts are human–machine command links and machine–human feedbacks. Such systems are found in medicine, [...] Read more.
Modern industrial, household and other equipment include sophisticated power mechanisms and complicated control solutions that require tighter human–machine–human interactions to form the structures known as cyber–physical–human systems. Their significant parts are human–machine command links and machine–human feedbacks. Such systems are found in medicine, e.g., in orthopedics, where they are important for the operation and functional abilities of orthopedic devices—wheelchair, prosthesis, rehabilitation units, etc. The mentioned feedbacks may be implemented based on the haptic perceptions that requires vibration actuators. In orthopedics, such actuators can be used also for diagnostic purposes. This research brings forward the idea of the use of 3D printing in conjunction with high quality permanent magnets. This allows for the achievement of better efficiency, smaller size, and the developing of actuators individually for particular circumstances. The obtained simulation, experimental data, and data about 3D manufacturing generally confirm the above hypothesis. In particular, the stiffness coefficient of the actuator’s membrane and attached mass, which can be changed easily during 3D printing, affects the frequency of maximal power output. Secondly, the 3D manufacturing process is quick, tunable and rather cheap. Finally, an elaboration of the design of the actuator that allows for the real-time modification of stiffness and mass in a program way is planned for future works. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding
Electronics 2019, 8(6), 667; https://doi.org/10.3390/electronics8060667 - 12 Jun 2019
Cited by 2
Abstract
For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the [...] Read more.
For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the influence of noise interference and realize accurate identification, a novel temporal-frequency autoencoding based method is proposed. In particular, the raw data is firstly transformed into the wavelet domain, where multi-level time-frequency representation is achieved. Then, a prior knowledge-based feature selection is proposed and applied to the transformed data to discard noise components and retain identity-related information simultaneously. Afterward, the stacked sparse autoencoder is introduced to learn intrinsic discriminative features from the selected data, and Softmax classifier is used to perform the identification task. The effectiveness of the proposed method is evaluated on two public databases, namely, ECG-ID and Massachusetts Institute of Technology-Biotechnology arrhythmia (MIT-BIH-AHA) databases. Experimental results show that our method can achieve high multiple-heartbeat identification accuracies of 98.87%, 92.3%, and 96.82% on raw ECG signals which are from the ECG-ID (Two-recording), ECG-ID (All-recording), and MIT-BIH-AHA database, respectively, indicating that our method can provide an efficient way for ECG biometric identification. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue
Electronics 2019, 8(6), 619; https://doi.org/10.3390/electronics8060619 - 01 Jun 2019
Cited by 2
Abstract
Surface electromyography (sEMG) applied to the sports training area makes possible the observation of fatigue as well as the generation of muscular strength, through the study of changes in signal characteristics, such as peak-to-peak amplitude, mean frequency and median, among others. In this [...] Read more.
Surface electromyography (sEMG) applied to the sports training area makes possible the observation of fatigue as well as the generation of muscular strength, through the study of changes in signal characteristics, such as peak-to-peak amplitude, mean frequency and median, among others. In this sense, this work presents the design of a portable prototype for the acquisition and processing of electromyographic (EMG) signals aimed at the detection of muscle fatigue in athletes. Using two Bluetooth Bee modules, a wireless communication was performed in order to send the muscular electrical activity of the skin surface to a user interface developed in LabVIEW. A group of players from the Volleyball team of the Universidad del Magdalena, performed a series of exercise routines with dynamic contractions and as they experienced fatigue, samples were taken of the contractions made. The tests were performed on the vastus lateralis and rectus femoris muscles. The analysis of fatigue under dynamic conditions of the two parameters studied, in frequency and time, showed that it is more pertinent to estimate fatigue indices in the frequency domain. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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Open AccessArticle
Multi-Channel Optoelectronic Measurement System for Soil Nutrients Analysis
Electronics 2019, 8(4), 451; https://doi.org/10.3390/electronics8040451 - 20 Apr 2019
Cited by 1
Abstract
To solve the problems that occur when farmers overuse chemical fertilizers, it is necessary to develop rapid and efficient portable measurement systems for the detection and quantification of nitrogen (N), phosphorus (P), and potassium (K) in soil. Challenges arise from the use of [...] Read more.
To solve the problems that occur when farmers overuse chemical fertilizers, it is necessary to develop rapid and efficient portable measurement systems for the detection and quantification of nitrogen (N), phosphorus (P), and potassium (K) in soil. Challenges arise from the use of currently available portable instruments which only have a few channels, namely measurement and the reference channels. We report on a home-built, multichannel, optoelectronic measurement system with automatically switching light sources for the detection of N, P, K content in soil samples. This optoelectronic measurement system consists of joint LED light sources with peak emission wavelengths of 405 nm, 660 nm, and 515 nm, a photodiode array, a circuit board with a microcontroller unit (MCU), and a liquid-crystal display (LCD) touch screen. The straightforward principle for rapid detection of the extractable nutrients (N, P, K) was well-established, and characterization of the designed measurement system was done. Using this multi-channel measurement system, available nutrients extracted from six soil samples could be measured simultaneously. The absorbance compensation, concentration calibration, and nutrition measurements were performed automatically to achieve high consistency across six channels. The experimental results showed that the cumulative relative standard deviations of 1.22%, 1.27%, and 1.00% were obtained from six channels with known concentrations of standard solutions, respectively. The coefficients of correlation for the detection of extracted nutrients of N, P, K content in soil samples using both the proposed method and conventional lab-based method were 0.9010, 0.9471, and 0.8923, respectively. Experimental results show that this optoelectronic measurement system can perform the measurement of N, P, K contents of six soil samples simultaneously and may be used as an actual tool in determining nutrients in soil samples with an improvement in detection efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Biometrics and its Applications)
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