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Biometrics Recognition Systems
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Biometrics Recognition Systems

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

Deadline for manuscript submissions: closed (30 July 2024) | Viewed by 18147

Special Issue Editors

Prof. Dr. Wei Wei

E-Mail Website
Guest Editor
Department of Computer Science, Xi'an University of Technology, Xi'an, China
Interests: wireless networks; wireless sensor networks application; image processing; mobile computing; distributed computing; pervasive computing; Internet of Things; and sensor data clouds
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Yan

E-Mail Website
Guest Editor
School of Information and Communication, Guilin University of Electronic Technology, Guilin, China
Interests: non-stationary signal analysis; feature extraction; abnormal state recognition
Prof. Shih-Hau Fang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
Interests: mobile positioning and applications; machine learning and pattern recognition; wireless network and communications; signal processing and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hsiao-Chun Wu

E-Mail Website
Guest Editor
Division of Electrical and Computer Engineering, School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: graph-based algorithms; topological analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric instrumentation and informatics have been emerging in the recent decades. The design of algorithms, systems, and architectures for biometrics recognition have become appealing to researchers. As diverse computing devices ranging from wearable smart phones to big cloud computing servers are becoming more available nowadays, the requirements for time/memory complexity and hardware costs may vary depending on different practical applications. In this Special Issue, we would like to encourage the development of novel ideas and designs for next-generation biometrics recognition technologies that are conscious of the requirements of reliability, robustness, cost-effectiveness, model transferability, and real-time operatability. New biomedical signals, sensors, systems, algorithms, and learning models are welcome. This Special Issue is open to all aspects of this topic, including theoretical analyses, experiments, and prototype implementation and has the aim of advancingthe future bioinformatics industry.

Prof. Dr. Wei Wei
Dr. Kun Yan
Prof. Shih-Hau Fang
Prof. Dr. Hsiao-Chun Wu
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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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • bioinformatics
  • intelligent systems
  • biological authentication
  • biomedical instrumentation
  • signal processing
  • artificial intelligence

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Published Papers (7 papers)

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Research

20 pages, 4950 KiB  
Article
Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles
by Amal Kammoun, Philippe Ravier and Olivier Buttelli
Sensors 2024, 24(16), 5318; https://doi.org/10.3390/s24165318 - 16 Aug 2024
Cited by 5 | Viewed by 1928
Abstract
The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and [...] Read more.
The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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24 pages, 4262 KiB  
Article
DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition
by Zijie Guo, Jian Guo, Yanan Huang, Yibo Zhang and Hengyi Ren
Sensors 2024, 24(15), 4779; https://doi.org/10.3390/s24154779 - 23 Jul 2024
Cited by 1 | Viewed by 1424
Abstract
Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, [...] Read more.
Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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18 pages, 4482 KiB  
Article
A Comprehensive Study of Object Tracking in Low-Light Environments
by Anqi Yi and Nantheera Anantrasirichai
Sensors 2024, 24(13), 4359; https://doi.org/10.3390/s24134359 - 5 Jul 2024
Cited by 3 | Viewed by 2048
Abstract
Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these [...] Read more.
Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance the tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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18 pages, 8506 KiB  
Article
FV-MViT: Mobile Vision Transformer for Finger Vein Recognition
by Xiongjun Li, Jin Feng, Jilin Cai and Guowen Lin
Sensors 2024, 24(4), 1331; https://doi.org/10.3390/s24041331 - 19 Feb 2024
Cited by 8 | Viewed by 2853
Abstract
In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. [...] Read more.
In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer’s self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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12 pages, 4403 KiB  
Article
Investigation of the Impact of Damaged Smartphone Sensors’ Readings on the Quality of Behavioral Biometric Models
by Paweł Rybka, Tomasz Bąk, Paweł Sobel and Damian Grzechca
Sensors 2022, 22(24), 9580; https://doi.org/10.3390/s22249580 - 7 Dec 2022
Cited by 1 | Viewed by 1811
Abstract
Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact [...] Read more.
Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact on UX (User Experience) is making its popularity rapidly increase among branches in the area of confidential data handling, such as banking, insurance companies, the government, or the military. Although behavioral biometric methods show a high degree of protection against fraudsters, they are susceptible to the quality of input data. The selected behavioral biometrics are strongly dependent on mobile phone IMU sensors. This paper investigates the harmful effects of gaps in data on the behavioral biometry model’s accuracy in order to propose suitable countermeasures for this issue. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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25 pages, 3669 KiB  
Article
Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features
by Kamil A. Kamiński and Andrzej P. Dobrowolski
Sensors 2022, 22(23), 9370; https://doi.org/10.3390/s22239370 - 1 Dec 2022
Cited by 5 | Viewed by 3816
Abstract
This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on [...] Read more.
This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known certified voice dataset. The ASR System owes this to the dual use of genetic algorithms both in the feature selection process and in the optimization of the system’s internal parameters. This was also influenced by the proprietary feature generation and corresponding classification process using Gaussian mixture models. This allowed the development of a system that makes an important contribution to the current state of the art in speaker recognition systems for telephone transmission applications with known speech coding standards. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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18 pages, 991 KiB  
Article
Using SincNet for Learning Pathological Voice Disorders
by Chao-Hsiang Hung, Syu-Siang Wang, Chi-Te Wang and Shih-Hau Fang
Sensors 2022, 22(17), 6634; https://doi.org/10.3390/s22176634 - 2 Sep 2022
Cited by 21 | Viewed by 3004
Abstract
Deep learning techniques such as convolutional neural networks (CNN) have been successfully applied to identify pathological voices. However, the major disadvantage of using these advanced models is the lack of interpretability in explaining the predicted outcomes. This drawback further introduces a bottleneck for [...] Read more.
Deep learning techniques such as convolutional neural networks (CNN) have been successfully applied to identify pathological voices. However, the major disadvantage of using these advanced models is the lack of interpretability in explaining the predicted outcomes. This drawback further introduces a bottleneck for promoting the classification or detection of voice-disorder systems, especially in this pandemic period. In this paper, we proposed using a series of learnable sinc functions to replace the very first layer of a commonly used CNN to develop an explainable SincNet system for classifying or detecting pathological voices. The applied sinc filters, a front-end signal processor in SincNet, are critical for constructing the meaningful layer and are directly used to extract the acoustic features for following networks to generate high-level voice information. We conducted our tests on three different Far Eastern Memorial Hospital voice datasets. From our evaluations, the proposed approach achieves the highest 7%–accuracy and 9%–sensitivity improvements from conventional methods and thus demonstrates superior performance in predicting input pathological waveforms of the SincNet system. More importantly, we intended to give possible explanations between the system output and the first-layer extracted speech features based on our evaluated results. Full article
(This article belongs to the Special Issue Biometrics Recognition Systems)
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