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Biometric Systems

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

Deadline for manuscript submissions: closed (7 January 2020) | Viewed by 115847

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Guest Editor
Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy
Interests: deep learning (ensembles of deep learners, transfer learning); computer vision (general-purpose image classifiers, medical image classification, texture descriptors); biometrics systems (fingerprint classification and recognition, signature verification, face recognition)
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Guest Editor
Information Technology& Cybersecurity Department, Missouri State University, Springfield, 901 South National Avenue, Springfield, MO 65804, USA
Interests: deep learning (ensembles of deep learners); medical image classification (general-purpose image classifiers, neonatal pain detection); biometrics systems (fingerprint classification and recognition, face recognition)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric recognition continues to be one of the most widely studied pattern recognition problems. The field is being driven in large part by the rise in hacking and the increasing need of advancing technological systems, such as the Internet and cellular phones, to secure personal identification. Biometric recognition is defined by several critical issues involved in the problem, such as quality checking of sensor inputs, biodata security, aliveness detection, and multimodal authentication. Regardless of the biometric chosen, all recognition systems must also isolate and extract a set of features in the biometric image or pattern that offers the greatest amount of information.

This Special Issue aims to highlight advances in machine learning as it relates to biometric recognition. Research papers on any of the critical issues involved, feature extraction and selection, and implementation problems and solutions are solicited.

Prof. Loris Nanni
Dr. Sheryl Brahnam
Guest Editors

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Keywords

  • Biometric systems
  • Multimodal authentication
  • Biodata security
  • Biometric features
  • Face recognition
  • Eye classification
  • Fingerprint classification

Published Papers (17 papers)

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17 pages, 4125 KiB  
Article
End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection
by Rami M. Jomaa, Hassan Mathkour, Yakoub Bazi and Md Saiful Islam
Sensors 2020, 20(7), 2085; https://doi.org/10.3390/s20072085 - 7 Apr 2020
Cited by 29 | Viewed by 4563
Abstract
Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach [...] Read more.
Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality. Full article
(This article belongs to the Special Issue Biometric Systems)
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20 pages, 6618 KiB  
Article
Wrist Vascular Biometric Recognition Using a Portable Contactless System
by Raul Garcia-Martin and Raul Sanchez-Reillo
Sensors 2020, 20(5), 1469; https://doi.org/10.3390/s20051469 - 7 Mar 2020
Cited by 17 | Viewed by 5659
Abstract
Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. [...] Read more.
Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system. Full article
(This article belongs to the Special Issue Biometric Systems)
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22 pages, 7882 KiB  
Article
Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition
by Mohsen Jenadeleh, Marius Pedersen and Dietmar Saupe
Sensors 2020, 20(5), 1308; https://doi.org/10.3390/s20051308 - 28 Feb 2020
Cited by 16 | Viewed by 3690
Abstract
Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease [...] Read more.
Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system. Full article
(This article belongs to the Special Issue Biometric Systems)
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21 pages, 3259 KiB  
Article
Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures
by Majd Abazid, Nesma Houmani and Sonia Garcia-Salicetti
Sensors 2020, 20(3), 933; https://doi.org/10.3390/s20030933 - 10 Feb 2020
Cited by 2 | Viewed by 3980
Abstract
We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories [...] Read more.
We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature. Full article
(This article belongs to the Special Issue Biometric Systems)
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21 pages, 2695 KiB  
Article
Ear Detection Using Convolutional Neural Network on Graphs with Filter Rotation
by Arkadiusz Tomczyk and Piotr S. Szczepaniak
Sensors 2019, 19(24), 5510; https://doi.org/10.3390/s19245510 - 13 Dec 2019
Cited by 13 | Viewed by 3768
Abstract
Geometric deep learning (GDL) generalizes convolutional neural networks (CNNs) to non-Euclidean domains. In this work, a GDL technique, allowing the application of CNN on graphs, is examined. It defines convolutional filters with the use of the Gaussian mixture model (GMM). As those filters [...] Read more.
Geometric deep learning (GDL) generalizes convolutional neural networks (CNNs) to non-Euclidean domains. In this work, a GDL technique, allowing the application of CNN on graphs, is examined. It defines convolutional filters with the use of the Gaussian mixture model (GMM). As those filters are defined in continuous space, they can be easily rotated without the need for some additional interpolation. This, in turn, allows constructing systems having rotation equivariance property. The characteristic of the proposed approach is illustrated with the problem of ear detection, which is of great importance in biometric systems enabling image based, discrete human identification. The analyzed graphs were constructed taking into account superpixels representing image content. This kind of representation has several advantages. On the one hand, it significantly reduces the amount of processed data, allowing building simpler and more effective models. On the other hand, it seems to be closer to the conscious process of human image understanding as it does not operate on millions of pixels. The contributions of the paper lie both in GDL application area extension (semantic segmentation of the images) and in the novel concept of trained filter transformations. We show that even significantly reduced information about image content and a relatively simple, in comparison with classic CNN, model (smaller number of parameters and significantly faster processing) allows obtaining detection results on the quality level similar to those reported in the literature on the UBEAR dataset. Moreover, we show experimentally that the proposed approach possesses in fact the rotation equivariance property allowing detecting rotated structures without the need for labor consuming training on all rotated and non-rotated images. Full article
(This article belongs to the Special Issue Biometric Systems)
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18 pages, 1270 KiB  
Article
Face Detection Ensemble with Methods Using Depth Information to Filter False Positives
by Loris Nanni, Sheryl Brahnam and Alessandra Lumini
Sensors 2019, 19(23), 5242; https://doi.org/10.3390/s19235242 - 28 Nov 2019
Cited by 6 | Viewed by 3294
Abstract
A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by [...] Read more.
A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by the ensemble. False positives are removed using different filtering steps based primarily on the characteristics of the depth map related to the subwindows of the whole image that contain candidate faces. A new filtering approach based on processing the image with different wavelets is also proposed here. The experimental results show that the applied filtering steps used in our best ensemble reduce the number of false positives without decreasing the detection rate. This finding is validated on a combined dataset composed of four others for a total of 549 images, including 614 upright frontal faces acquired in unconstrained environments. The dataset provides both 2D and depth data. For further validation, the proposed ensemble is tested on the well-known BioID benchmark dataset, where it obtains a 100% detection rate with an acceptable number of false positives. Full article
(This article belongs to the Special Issue Biometric Systems)
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12 pages, 3689 KiB  
Article
Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification
by Matthieu Saumard, Marwa Elbouz, Michaël Aron, Ayman Alfalou and Christian Brosseau
Sensors 2019, 19(23), 5092; https://doi.org/10.3390/s19235092 - 21 Nov 2019
Cited by 1 | Viewed by 3127
Abstract
Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize [...] Read more.
Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates. Full article
(This article belongs to the Special Issue Biometric Systems)
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25 pages, 6416 KiB  
Article
Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset
by Christof Kauba, Bernhard Prommegger and Andreas Uhl
Sensors 2019, 19(22), 5014; https://doi.org/10.3390/s19225014 - 17 Nov 2019
Cited by 29 | Viewed by 6479
Abstract
Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated [...] Read more.
Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated capturing device design is important for good image quality and, furthermore, at a decent recognition rate. We propose a novel contactless capturing device design, including technical details of its individual parts. Our capturing device is suitable for finger and hand vein image acquisition and is able to acquire palmar finger vein images using light transmission as well as palmar hand vein images using reflected light. An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well. Full article
(This article belongs to the Special Issue Biometric Systems)
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23 pages, 3492 KiB  
Article
Online Signature Verification Based on a Single Template via Elastic Curve Matching
by Huacheng Hu, Jianbin Zheng, Enqi Zhan and Jing Tang
Sensors 2019, 19(22), 4858; https://doi.org/10.3390/s19224858 - 7 Nov 2019
Cited by 7 | Viewed by 4039
Abstract
Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, [...] Read more.
Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained. Full article
(This article belongs to the Special Issue Biometric Systems)
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20 pages, 10529 KiB  
Article
Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification
by Changxin Gao, Jin Wang, Leyuan Liu, Jin-Gang Yu and Nong Sang
Sensors 2019, 19(18), 3861; https://doi.org/10.3390/s19183861 - 6 Sep 2019
Cited by 5 | Viewed by 2646
Abstract
Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to [...] Read more.
Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space–time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a superpixel-based representation to improve spatial alignment. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Biometric Systems)
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14 pages, 4998 KiB  
Article
Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information
by Yiding Wang, Heng Cao, Xiaochen Jiang and Yuanyan Tang
Sensors 2019, 19(17), 3718; https://doi.org/10.3390/s19173718 - 28 Aug 2019
Cited by 12 | Viewed by 2929
Abstract
The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was [...] Read more.
The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%. Full article
(This article belongs to the Special Issue Biometric Systems)
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23 pages, 4303 KiB  
Article
Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification
by Lukas Kolda, Ondrej Krejcar, Ali Selamat, Kamil Kuca and Oluwaseun Fadeyi
Sensors 2019, 19(17), 3709; https://doi.org/10.3390/s19173709 - 26 Aug 2019
Cited by 5 | Viewed by 3194
Abstract
Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed [...] Read more.
Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings. Full article
(This article belongs to the Special Issue Biometric Systems)
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8 pages, 2012 KiB  
Article
ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison
by Mariusz Pelc, Yuriy Khoma and Volodymyr Khoma
Sensors 2019, 19(10), 2350; https://doi.org/10.3390/s19102350 - 22 May 2019
Cited by 25 | Viewed by 3991
Abstract
In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients [...] Read more.
In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy. Full article
(This article belongs to the Special Issue Biometric Systems)
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15 pages, 4186 KiB  
Article
Novel Local Coding Algorithm for Finger Multimodal Feature Description and Recognition
by Shuyi Li, Haigang Zhang, Yihua Shi and Jinfeng Yang
Sensors 2019, 19(9), 2213; https://doi.org/10.3390/s19092213 - 13 May 2019
Cited by 21 | Viewed by 3729
Abstract
Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. [...] Read more.
Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency. Full article
(This article belongs to the Special Issue Biometric Systems)
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17 pages, 1251 KiB  
Article
A Two-Stage Method for Online Signature Verification Using Shape Contexts and Function Features
by Yu Jia, Linlin Huang and Houjin Chen
Sensors 2019, 19(8), 1808; https://doi.org/10.3390/s19081808 - 16 Apr 2019
Cited by 19 | Viewed by 4350
Abstract
As a behavioral biometric trait, an online signature is extensively used to verify a person’s identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. [...] Read more.
As a behavioral biometric trait, an online signature is extensively used to verify a person’s identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. Specifically, in the first stage, features of shape contexts are extracted from the input and classification is made based on distance metric. Only the inputs passing by the first stage are represented by a set of function features and verified. To improve the matching accuracy and efficiency, we propose shape context-dynamic time warping (SC-DTW) to compare the test signature with the enrolled reference ones based on the extracted function features. Then, classification based on interval-valued symbolic representation is employed to decide if the test signature is a genuine one. The proposed method is evaluated on SVC2004 Task 2 achieving an Equal Error Rate of 2.39% which is competitive to the state-of-the-art approaches. The experiment results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Biometric Systems)
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Review

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36 pages, 7362 KiB  
Review
Face Recognition Systems: A Survey
by Yassin Kortli, Maher Jridi, Ayman Al Falou and Mohamed Atri
Sensors 2020, 20(2), 342; https://doi.org/10.3390/s20020342 - 7 Jan 2020
Cited by 323 | Viewed by 49332
Abstract
Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. [...] Read more.
Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition. Full article
(This article belongs to the Special Issue Biometric Systems)
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19 pages, 314 KiB  
Review
Sensor-Based Technology for Social Information Processing in Autism: A Review
by Andrea E. Kowallik and Stefan R. Schweinberger
Sensors 2019, 19(21), 4787; https://doi.org/10.3390/s19214787 - 4 Nov 2019
Cited by 14 | Viewed by 5089
Abstract
The prevalence of autism spectrum disorders (ASD) has increased strongly over the past decades, and so has the demand for adequate behavioral assessment and support for persons affected by ASD. Here we provide a review on original research that used sensor technology for [...] Read more.
The prevalence of autism spectrum disorders (ASD) has increased strongly over the past decades, and so has the demand for adequate behavioral assessment and support for persons affected by ASD. Here we provide a review on original research that used sensor technology for an objective assessment of social behavior, either with the aim to assist the assessment of autism or with the aim to use this technology for intervention and support of people with autism. Considering rapid technological progress, we focus (1) on studies published within the last 10 years (2009–2019), (2) on contact- and irritation-free sensor technology that does not constrain natural movement and interaction, and (3) on sensory input from the face, the voice, or body movements. We conclude that sensor technology has already demonstrated its great potential for improving both behavioral assessment and interventions in autism spectrum disorders. We also discuss selected examples for recent theoretical questions related to the understanding of psychological changes and potentials in autism. In addition to its applied potential, we argue that sensor technology—when implemented by appropriate interdisciplinary teams—may even contribute to such theoretical issues in understanding autism. Full article
(This article belongs to the Special Issue Biometric Systems)
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