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Sensors for Biometric Recognition and Authentication

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

Deadline for manuscript submissions: 10 November 2024 | Viewed by 9976

Special Issue Editors


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Guest Editor
Computer Science Department, GREYC-UMR 6072 Research Lab, University of Caen Nomandy, 14000 Caen, France
Interests: biometrics; computer vision; video analysis; human behavior understanding; image processing; machine learning

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Guest Editor
IMT Nord Europe, Institut Mines-Télécom, Center for Digital Systems, 59000 Lille, France
Interests: computer vision; machine learning; biometrics; shape analysis

Special Issue Information

Dear Colleagues,

In addition to computing power and improved sensors capable of capturing novel biological signals such as heartbeat and brain waves via EEG or EKG, behavioral analysis and activity recognition are increasingly being used for a variety of purposes from healthcare to law enforcement. An important trend is the development of multimodal biometrics and the increasing use of biometrics, focusing on various behavioral patterns.  In addition to traditional biometric methods such as face recognition, biometric methods also include gesture dynamics, gait features, and behavioral characteristics, as long as the behavior is analyzed to determine the genetic, physical, physiological, behavioral or emotional nature characterizing a specific individual.

This special issue focuses on new biometric modalities and recent developments in behavioral biometrics that rely on specific data technologies related to the physical, physiological, or behavioral aspects of the human body (including when in motion). Its purpose is to highlight recent advances in the development of human authentication technologies and the categorization of humans based on physiological characteristics (including predicting future behavior).

Topics to be covered including, but not limited to:

  • Machine learning for biometrics
  • Machine learning for behavioral recognition
  • Biometric Fusion framework
  • A comparative study of the existing learning approaches of Behavioral Biometric Datasets

Dr. Youssef Chahir
Dr. Hassen Drira
Guest Editors

Manuscript Submission Information

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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

  • behavioral recognition
  • gait recognition
  • gesture recognition
  • soft biometrics
  • physiological biometrics
  • biometric categorization
  • facial attribute recognition
  • gender recognition
  • age estimation
  • model-free approaches

Published Papers (5 papers)

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Research

35 pages, 4940 KiB  
Article
A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers
by Mostafa E. A. Ibrahim, Qaisar Abbas, Yassine Daadaa and Alaa E. S. Ahmed
Sensors 2024, 24(1), 15; https://doi.org/10.3390/s24010015 - 19 Dec 2023
Cited by 1 | Viewed by 1301
Abstract
Biometric authentication is a widely used method for verifying individuals’ identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed [...] Read more.
Biometric authentication is a widely used method for verifying individuals’ identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model’s performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication. Full article
(This article belongs to the Special Issue Sensors for Biometric Recognition and Authentication)
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23 pages, 1342 KiB  
Article
Exploring Self-Supervised Vision Transformers for Gait Recognition in the Wild
by Adrian Cosma, Andy Catruna and Emilian Radoi
Sensors 2023, 23(5), 2680; https://doi.org/10.3390/s23052680 - 1 Mar 2023
Cited by 4 | Viewed by 2345
Abstract
The manner of walking (gait) is a powerful biometric that is used as a unique fingerprinting method, allowing unobtrusive behavioral analytics to be performed at a distance without subject cooperation. As opposed to more traditional biometric authentication methods, gait analysis does not require [...] Read more.
The manner of walking (gait) is a powerful biometric that is used as a unique fingerprinting method, allowing unobtrusive behavioral analytics to be performed at a distance without subject cooperation. As opposed to more traditional biometric authentication methods, gait analysis does not require explicit cooperation of the subject and can be performed in low-resolution settings, without requiring the subject’s face to be unobstructed/clearly visible. Most current approaches are developed in a controlled setting, with clean, gold-standard annotated data, which powered the development of neural architectures for recognition and classification. Only recently has gait analysis ventured into using more diverse, large-scale, and realistic datasets to pretrained networks in a self-supervised manner. Self-supervised training regime enables learning diverse and robust gait representations without expensive manual human annotations. Prompted by the ubiquitous use of the transformer model in all areas of deep learning, including computer vision, in this work, we explore the use of five different vision transformer architectures directly applied to self-supervised gait recognition. We adapt and pretrain the simple ViT, CaiT, CrossFormer, Token2Token, and TwinsSVT on two different large-scale gait datasets: GREW and DenseGait. We provide extensive results for zero-shot and fine-tuning on two benchmark gait recognition datasets, CASIA-B and FVG, and explore the relationship between the amount of spatial and temporal gait information used by the visual transformer. Our results show that in designing transformer models for processing motion, using a hierarchical approach (i.e., CrossFormer models) on finer-grained movement fairs comparatively better than previous whole-skeleton approaches. Full article
(This article belongs to the Special Issue Sensors for Biometric Recognition and Authentication)
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13 pages, 5561 KiB  
Article
Double-Center-Based Iris Localization and Segmentation in Cooperative Environment with Visible Illumination
by Jiangang Li and Xin Feng
Sensors 2023, 23(4), 2238; https://doi.org/10.3390/s23042238 - 16 Feb 2023
Cited by 3 | Viewed by 1800
Abstract
Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the [...] Read more.
Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the input eye image; however, iris images that have been captured in user non-cooperative and visible illumination environments often suffer from adverse noise (e.g., light reflection, blurring, and glasses occlusion), which challenges many existing segmentation-based parameter-fitting localization methods. To address this problem, we propose a novel double-center-based end-to-end iris localization and segmentation network. Different from many previous iris localization methods, which use massive post-process methods (e.g., integro-differential operator-based or circular Hough transforms-based) on iris or contour mask to fit the inner and outer circles, our method directly predicts the inner and outer circles of the iris on the feature map. In our method, an anchor-free center-based double-circle iris-localization network and an iris mask segmentation module are designed to directly detect the circle boundary of the pupil and iris, and segment the iris region in an end-to-end framework. To facilitate efficient training, we propose a concentric sampling strategy according to the center distribution of the inner and outer iris circles. Extensive experiments on the four challenging iris data sets show that our method achieves excellent iris-localization performance; in particular, it achieves 84.02% box IoU and 89.15% mask IoU on NICE-II. On the three sub-datasets of MICHE, our method achieves 74.06% average box IoU, surpassing the existing methods by 4.64%. Full article
(This article belongs to the Special Issue Sensors for Biometric Recognition and Authentication)
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20 pages, 8685 KiB  
Article
Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System
by Robert Chen-Hao Chang, Chia-Yu Wang, Yen-Hsing Li and Cheng-Di Chiu
Sensors 2023, 23(4), 2184; https://doi.org/10.3390/s23042184 - 15 Feb 2023
Cited by 2 | Viewed by 1661
Abstract
In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge computing has [...] Read more.
In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge computing has become popular for the sake of protecting user privacy. In this paper, we propose a low-complexity and lightweight convolutional neural network (CNN) and we design intellectual property (IP) for shortening the inference time in finger vein recognition. This neural network system can operate independently in client mode. After fetching the user’s finger vein image via a near-infrared (NIR) camera mounted on an embedded system, vein features can be efficiently extracted by vein curving algorithms and user identification can be completed quickly. Better image quality and higher recognition accuracy can be obtained by combining several preprocessing techniques and the modified CNN. Experimental data were collected by the finger vein image capture equipment developed in our laboratory based on the specifications of similar products currently on the market. Extensive experiments demonstrated the practicality and robustness of the proposed finger vein identification system. Full article
(This article belongs to the Special Issue Sensors for Biometric Recognition and Authentication)
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14 pages, 3597 KiB  
Article
Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification
by Yinyin Wei, Xiangyang Zhang, Aijun Zeng and Huijie Huang
Sensors 2022, 22(20), 7723; https://doi.org/10.3390/s22207723 - 12 Oct 2022
Cited by 5 | Viewed by 1944
Abstract
Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. [...] Read more.
Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively. Full article
(This article belongs to the Special Issue Sensors for Biometric Recognition and Authentication)
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