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Owing to its capability to offer remote services, the Internet of Things (IoT) has immersed itself in all areas of our daily lives. However, this big use of IoT networks makes the user's data change insecurely in open channels vulnerable to malicious use. As a result, the security of the user's data in an IoT environment becomes a critical issue. Given that authentication is a mechanism that may prevent hackers from retrieving and exploiting data communicated between IoT devices, researchers have proposed many lightweight IoT authentication schemes in the last decades. However, most of these schemes are based on two authentication factors and are unable to ensure unlink ability, key secrecy, perfect forward secrecy, and resistance to node capture, denial of service (DoS) attacks, stolen verifiers, denning-SSACO attacks, and GWN bypassing. In this paper, we present an anonymous three-factor authentication scheme based on elliptic curve cryptography (ECC), which can provide all security services and resist well-known attacks. Then, based on informal security analysis and the formal security proof using ProVerif we show that our provided scheme is secure and can resist known attacks. Finally, we show the comparison result among our protocol and other protocols in terms of computation overheads, communication overheads, and security features. [ABSTRACT FROM AUTHOR]

Emotions fluctuate during the process of social interaction. Although the co-creation of emotions through organizational behavior has been discussed theoretically in existing research, there is no method to demonstrate how emotions are co-created. Instead, previous studies have paid much attention to empathy, in which a person's emotions are contagious. In contrast to self-report, which is a traditional method that can only assess emotions at a single point in time and adapts to empathy, biometric technology has made it possible to analyze emotional fluctuations over time. However, previous studies have focused only on understanding the emotional fluctuations of individuals separately. In the present study, we developed a system to measure the co-creation of emotions using a wearable device. The pulse rate was converted into valence as a positive–negative emotion, and the fluctuations in valence were analyzed by cross-correlation. We demonstrated the feasibility of the proposed system through triangulation by integrating biometrics with observation and self-report. The proposed system was verified to measure the co-creation of pair and group emotions using real-world data beyond laboratory settings. The present study contributes to business administration by proposing a critical concept for measuring the co-creation of emotions based on a constructionist approach. [ABSTRACT FROM AUTHOR]

This paper investigates the use of continuous morse wavelet transform (CWT) coefficients as inputs to convolutional neural networks (CNNs) for electrocardiogram (ECG) biometric authentication. We evaluate the performance and generalization of pre-trained SqueezeNet architecture using the ECG-ID Database. Our approach involves extracting 10 scalograms from each subject's ECG signals and employing gradient descent optimization during training. The models demonstrate high accuracy, achieving over 90% on both training and validation datasets, indicating robust performance and minimal overfitting. Further analysis using the F1 confidence curve and ROC curve reveals a balanced trade-off between precision and recall, with an optimal F1 score of 0.84 and an AUC of 0.84, respectively. Additionally, we explore the impact of different CWT parameter settings, including Voice per Octave (VPO), symmetry parameter (gamma), and time-bandwidth product (P2). The optimal VPO of 41 yields an AUC of 0.87 and an F1 score of 0.84. The best performance is achieved with gamma values greater than 2 and time-bandwidth products between 45 and 80, enhancing time localization and frequency resolution. In this study, the significance of fine-tuning wavelet parameters to improve the effectiveness of ECG biometric systems is demonstrated, demonstrating the potential of combining CWT and CNNs for reliable biometric authentication. [ABSTRACT FROM AUTHOR]

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person's finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification. [ABSTRACT FROM AUTHOR]

The widespread use of iris recognition systems has led to a growing demand for enhanced security measures to counter potential iris presentation attacks, also known as anti-spoofing. To enhance the security and reliability of iris recognition systems, researchers have developed numerous methods for detecting presentation attacks. Most of these methods lack precision in detecting unknown attacks compared to known attacks. In addition, most literature on iris presentation attack detection (PAD) systems utilizes near-infrared (NIR) samples as inputs. These samples produce superior-quality and robust images with less reflection in the cornea of the eye. Despite this, due to the widespread use of smartphones and the necessity for unsupervised identity verification, visible-light samples play a crucial role in detecting presentation attacks. These samples can be easily captured using smartphone cameras. In this paper, a dual-spectral attention model has been developed to train a unified model for multiple real-world attack scenarios. Two different scenarios were tested. In the first scenario, the model was trained as a one-class anomaly detection (AD) approach, while in the second scenario, it was trained as a normal two-class detection approach. This model achieved the best result for the attack presentation classification error rate (APCER) of 4.87% in a one-class AD scenario when tested on the attack dataset, outperforming most studies on the same test dataset. These experimental results suggest that future research opportunities in areas such as working with visible light images, using an AD approach, and focusing on uncontrolled environment samples and synthetic iris images may improve iris detection accuracy [ABSTRACT FROM AUTHOR]

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user's iris feature vector is encrypted and stored in the cloud. During the matching process, the user's iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy. [ABSTRACT FROM AUTHOR]

External biometrics such as thumbprint and facial recognition have become standard tools for securing our digital devices and protecting our data. These systems, however, are potentially prone to copying and cybercrime access. Researchers have therefore explored internal biometrics, such as the electrical patterns within an electrocardiogram (ECG). The heart's electrical signals carry sufficient distinctiveness to allow the ECG to be used as an internal biometric for user authentication and identification. Using the ECG in this way has many potential advantages and limitations. This article reviews the history of ECG biometrics and explores some of the technical and security considerations. It also explores current and future uses of the ECG as an internal biometric. [ABSTRACT FROM AUTHOR]

In current existences, many techniques have been developed to cast votes. The existing scenario is tremendously aiming towards the development of an efficient voting system, which ensures individual authentication and reduces malpractices. It is a challenging perspective to recognize each and every individual person casting the vote. The proposed work considers this scenario and developed a controller based electronic voting machine using R305 Fingerprint sensor for biometric authentication of every individual. The controlled electronic voting machine gives ample scope to cast the votes in simpler manner without minimizing the efficient results. The proposed digital Biometric-based EVM is presented in this work. The processed flow for creating this biometric based EVM is implemented on FPGA board using Verilog software on Xilinx ISE. The experimental results produce efficient verification of unique authentication on selections of political parties. [ABSTRACT FROM AUTHOR]

Background: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. Objective: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. Methods: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. Results: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r =0.23; P =.01). In addition, age (r =0.30; P =.001), BMI (r =0.19; P =.03), and the severity of symptoms (r =0.19; P =.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R 2=0.1; β =1.6; P =.01). Conclusions: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables. [ABSTRACT FROM AUTHOR]

The Electrocardiogram records the heart's electrical signals. It is a practice; a painless diagnostic procedure used to rapidly diagnose and monitor heart problems. The ECG is an easy, noninvasive method for diagnosing various common heart conditions. Due to its unique advantages that other humans do not share, in addition to the fact that the heart's electrical activity may be easily detected from the body's surface, security is another area of concern. On this basis, it has become apparent that there are essential steps of pre-processing to deal with data of an electrical nature, signals, and prepare them for use in Biometric systems. Since it depends on the structure and function of the heart, it can be utilized as a biometric attribute for identification and recognition. This academic paper presents a new data formatting technique to be used in biometric systems based on the two major dataset kinds, PTB and PTB-XL. [ABSTRACT FROM AUTHOR]

Cloud computing becomes an important application development platform for processing user data with high security. Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner. Thus, ensuring the security and confidentiality of the data while processing in the centralized network is very difficult. The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network. Intruders are very active over the network like real authenticated user to hack the personal sensitive data, such as bank balance, health data, personal data, and confidential documents over the cloud network. In this research, a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in M-Healthcare. Conditions of patients are monitored using wireless sensor devices and are then transferred to the server. Kerberos protocol helps in avoiding unnecessary communication of authenticated data over the cloud network. Biometric security process is a procedure with the best security in most of the authentication field. Trust node is responsible in carrying data packets from the sender to the receiver in the cloud network. The Kerberos protocol is used in trust node to ensure security. Secured communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form, which refers to the fingerprint image of both sender and receiver. The computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods. [ABSTRACT FROM AUTHOR]

The present paper suggests a hybrid solution to address two key authentication challenges: data privacy and the limitations of mobile devices resources. The former is addressed using Partially Homomorphic Encryption based on Paillier Algorithm for the encryption. While the latter is handled using a combination of Deep Convolutional Neural Network and Local Ternary Pattern for face recognition. We compare the accuracy and performance of our proposed solution to others proposed by the literature on ORL dataset and Extended Yale data set. Our findings suggest our proposed methods return higher recognition rates including 98.75% on encrypted ORL data set and 98.78% on encrypted Extended Yale data. In contrast, the existing methods achieved lower recognition rates, which have achieved only 92.50% and 95.44% of recognition rates on encrypted ORL and Extended Yale data set, respectively. [ABSTRACT FROM AUTHOR]

Biometric verification is a process to authenticate whether the subject is what it claims to be, based on the characteristics of the human body. These characteristics must meet seven (7) conditions to enable them to be used in a practical biometric system. These conditions are namely distinctiveness, performance, collectability, acceptability, universality, circumvention, and permanence. Electrocardiogram (ECG) is a human body characteristic measured from the heart that meets these seven conditions. Choosing the correct features from the ECG is important to get a high authentication rate. This paper proposed a new algorithm known as Bipolar Slope Feature (BSF) for ECG features selection. It is developed based on the relationship of slopes between several locations in a complete ECG cycle. The Receiver Operating Characteristic (ROC) curve is used to measure the effectiveness of this technique for the application of biometric verification. [ABSTRACT FROM AUTHOR]

Spoofing attacks on the fingerprint scanners become major and serious concern with the growing development and use of biometric technologies. Level 1 and Level 2 features; which are said to be unique and most commonly used features in fingerprint verification systems, are easily get spoofed. Moreover, single feature based specifically designed spoof detection methods are not performed well on different fingerprint scanners and spoofing materials. This paper proposed the fusion of pores perspiration and texture features in static software based approach to identify live and fake fingerprints. The pores perspiration activity is quantified by computing the ridge signal energy and gray level distributions around the detected pores. These pore characteristics are statically determined instead of dynamic measurement. Autoencoder neural network is used to reduce the high dimensional feature vector and learn its low dimensional hidden representation unsupervisedly. The binary classification in two classes: live and spoof is performed by the supervisedly trained softmax classifier. The performance of the classifier is evaluated in terms of Average Classification Error (ACE) and misclassification rates: FerrLive and FerrFake. The experimental results carried out on LivDet 2013 and LivDet 2015 databases show the improvement of the classifier performance in comparison to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Smart grid (SG) provides a suitable adjustment in the amount of power generation by providing the ability to supervise consumer behavior. SG uses in the smart system to encourage cultural heritage because it is accountable for providing power without any interruption. SG is one of the vital components to authorize smart systems with a lot of smart features to attract visitors to come and visit heritage. In SG, environment security and privacy are the major concern for communications. An authentication protocol provides secure communication between users and service provider for security and privacy purpose. Several authentication protocols are available in the literature. However, they are enabled to known security attacks easily or they are not computationally efficient for SG communication. In the present paper, we design an ECC-based mutual authentication protocol for smart grid communication using biometric approach. The present framework satisfy various security features such as replay attack, user anonymity, man in the middle attack, key freshness, message authentication, session key agreement, impersonation attack, non-traceability and non-transferability. Further, the proposed protocol takes much less communication and computation costs compared with other existing protocols in SG environment. Therefore, our scheme is convenient for practical application in SG communication. [ABSTRACT FROM AUTHOR]

In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods. [ABSTRACT FROM AUTHOR]

The biometric characteristics of Trachinus araneus , Trachinus draco and Trachinus radiatus from the Egyptian Mediterranean waters at the West of Alexandria City, were studied. In total, 105, 96 and 55 specimens of these three Fish species were sampled, respectively, by the use of bottom trawls operated in the sectors of El-Dabaa and Sidi Abd El Rahman. The morphological characteristic and related index ratio were determined. The results showed that T. araneus total length varied from 10.9 to 30.0 cm TL with mean length of 19.87 ± 5.43 cm TL and a number of horizontal dots appear to be distributed along the lateral line, T. draco total length was 11.8 to 27.6 cm TL with a mean length of 17.85 ± 4.23 cm TL, and specimens of this Fish species showed yellow vertical oblique lines on the body. On the other hand, T. radiatus is characterized by a total length of 10.6 to 35.0 cm TL with a mean length 18.04 ± 4.87 cm TL, and by circular brown dots spread on the whole body and head. Furthermore, these three Fish species have poisonous spines, one on each operculum, and both spines on the internal border of the dorsal part of each eye orbit. The morphometric regression of each morphometric character showed close agreement between the observed and calculated values. [ABSTRACT FROM AUTHOR]

Mobile learning is an extended version of e-learning. The revolution of handheld devises had encouraged to design courses to be compatible with different mobiles versions, brands and platforms. Mobile exams need more attention to strengthen mobile learning certificates. A major issue with mobile exam is student authentication and identification before and during exam session. With no presence of proctor, more techniques are available to ensure student identification. In this paper, we proposed a model that uses a tradition user name and password in addition to the biometric iris recognition technique to verify student identity before and during mobile exam. [ABSTRACT FROM AUTHOR]

The focus of this study is to assess the research output of Biometric publications from the period of 2010 - 2019 through scientometric analysis. The total number of records were 7335 which is from Web of Science and analyzed using various tools like Histcite, Bibexcel and interpreted using Microsoft Excel and Google sheets. The laws of bibliometrics are also analyzed along with Relative growth rate, doubling time, Degree of collaboration and Collaborative indexes is also interpreted. [ABSTRACT FROM AUTHOR]

3D Palmprint recognition has become a promising alternative tool for resolving problems compared to the robustness of 2D palmprint recognition. Regarding robustness, biometric systems that use 2D Palmprint suffer from being attacked by using a fake Palmprint identical. Given this, the current paper introduces a new 3D Palmprint recognition approach. Firstly, a set of preprocessing techniques has been applied on 3D depth image such as Tan and Triggs method which can effectively and efficiently eliminate the effect of the low-frequency component with keeping the local statistical properties of the processed image. Then, Gabor wavelets have been employed to extract features. After that, the extracted features have been used as an input in the collaborative representation based classification with regularized least squares (CRC_RLS) to classify the 3D Palmprint images. To evaluate its performance, the proposed algorithm has been applied on the PolyU 3D Palmprint database which contains 8.000 samples. The experimental results successfully and greatly improve the recognition results, especially when, we use Tan and Triggs method for preprocessing and Gabor for feature extraction with CRC_RLS for presentation and classification. We achieve a significant recognition rate of 100 % in lowest Runtime which reflects the robustness of the proposed recognition system. [ABSTRACT FROM AUTHOR]

In the past decade, i-vector shows promising result in speaker recognition by modelling a total variability subspace. In this study, as ECG is analogous to speech signals, we focused on the performance of i-vector models based on various compensation methods for ECG identification system in order to combat variability issues. The performance of the models is evaluated based on unbiased protocol (protocol 1) and all-subject protocol (protocol 2) for different compensation methods, including whitening, Linear Discriminate Analysis (LDA), Within-Class Covariance Normalization (WCCN) for single approach, and WCCN-whitening, LDA-whitening, WCCN-LDA for sequential approach. ECG-ID database from Physionet is used for evaluation. It contains 90 subjects with 310 ECG recordings. From the experimental results, we observed that sequential approach has overwhelmed the single approach. i-vector with WCCN-LDA model showed the best rank-1 performance among all the compensation methods for both protocols, 91.89% and 88.89% respectively. In contrast, LDA achieved 67.11% and 64.44% for protocol 1 and 2 respectively, which is the worst among others. We also observed that, the sequential approach requires lesser Gaussian components, consistently 16 components compared to single compensation techniques which requires 32 or more components to achieve the best result hence this shortens the system computation time. [ABSTRACT FROM AUTHOR]

This paper proposes a new local image descriptor for Finger Knuckle Print Recognition Systems (FKPRS), named Log-Gabor Binarized Statistical Image Features descriptor (LGBSIF). The idea of LGBSIF is based on the image Log-Gabor wavelet representation and the Binarized Statistical Image Features (BSIF). Initially, the Region of Interest (ROI) of the FKP images are analyzed with a 1D Log-Gabor wavelet to extract the preliminary features that are presented by both the real and imaginary parts of the filtered image. The main motive of the LGBSIF is to enhance the Log-Gabor real and imaginary features by applying the BSIF coding method. Secondly, histograms extracted from the encoded real and imaginary images respectively are concatenated in one large feature vector. Thirdly, the PCA+LDA technique is used to reduce the dimensionality of this feature and enhance its discriminatory power. Finally, the Nearest Neighbor Classifier that uses the Cosine distance is employed for the matching process. The evaluation of the performance of the proposed system is done on the Poly-U FKP database. However, the experimental results have shown that the proposed system achieves better results than other state-of-the-art systems and confirmed the tenacity of the proposed descriptor. Further, the results also prove that the performance efficiency of the introduced system in terms of recognition rate (Rank1) and equal error rate (EER) are 100% and 0% for both modes of identification and verification respectively. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a rising the innovation that demonstrates incredible guarantees for the different complex applications for both mass open and the military. The detecting for innovation attached with the preparing energy and the remote correspondence the security of biometric data might be compromised by system assaults, showing person's data without an appropriate insurance isn't reasonable for approval. The concern of the remote correspondence of the innovation additionally causes different kind of security and dangers in the MANET. The expectation of this paper to examine of the security connected issues and difficulties in the remote sensor organizations utilizing MANET. It is distinguish the security and dangers of survey arranged security instruments for the remote sensor organization. Notwithstanding, encryption and decoding calculations are moderate and contain tedious activities for moving information in system. In this section, by considering remote sensor arranges and confirming server is present another idea called "review" and manages its productivity in managing the security issue in MANET. A "digest" can be gotten from any sort of data attribute through which no one can catch any data of essential biometric qualities. Here demonstrate that this idea prompts the expansion of the precision and availability of a biometric framework. [ABSTRACT FROM AUTHOR]

The human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore, it is very crucial to recovering the footprints from identifying the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time the world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defence, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints quickly. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification. [ABSTRACT FROM AUTHOR]

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Transportation plays a vital role in ones life. The main goal of this paper is to eradicate the day to day and one of the major problems with regard to carrying a ticket during transportation from ones life and make traveling a lot more peaceful. For this purpose, we are proposing a biometric-based ticketing system in the metro railway scenario but not limited to the same. In order to get a unique identifier for each person, we are considering their fingerprint right away from registration, booking tickets and validating the fingerprint on the day of the journey so he/she can travel on a particular day and on the desired train to his/her preferred destination. The fingerprint sensor will be interfaced with Arduino which in turn will store the fingerprint data to the cloud. We are proposing a two-way encryption standard for storing the sensitive fingerprint data in the cloud. This two-way encryption standard involves encrypting the data during data generation at the hardware end and encrypting it again before storing it in the cloud database. [ABSTRACT FROM AUTHOR]

This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject. Correlation model has yielded great significant difference of coefficient between autocorrelation and crosscorrelation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition. [ABSTRACT FROM AUTHOR]

Due to the ease of handling with low resolution images, low cost hardware and high recognition accuracy, palmprint recognition has been attracting in the recent years. Many different features extracted of the palmprint image have been employed to perform the recognition tasks. Most of the recognition efforts consequently has been put on the designing and obtaining a relevant set of effective hand-crafted features which is considered to be the drawback of the traditional image based biometric recognition systems. To overcome the aforementioned problem, in this paper we explore the applicability of MobileNet V2 deep convolutional neural networks on palmprint recognition by fine-tuning a pretrained MobileNet neural networks. We also explore the performance of dropout support vector machines (SVM) by training them on the deep features of the similar pretrained networks. The experiments are carried out using datasets provided by Hong Kong Polytechnic University of Science and Technology. The dataset consists of 6000 grayscale images of 128x128 pixels from 500 different palms. It is demonstrated that the proposed schemes exhibit state-of-the-art performance on the datasets. The second scheme, MobileNet V2 based features with SVM classifier, is able to achieve best average testing and validation accuracy rate of 100% outperforming the best previous reported results. [ABSTRACT FROM AUTHOR]

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. • Presenting vital concepts in WBANs' security vulnerabilities and the required services. • Describing the main components of ECG signals. • Presenting a taxonomy of ECG signal-based security solutions: cryptography, steganography, and ECCG signal security schemes included. • Illustrating their core architecture, describing main services, and highlighting key features. • Highlighting open problems and remaining challenges in the ECG-based security context. [ABSTRACT FROM AUTHOR]

This essay focuses on elections in Kenya and analyses the use of technology and the exploitation of personal data in both the electoral process and campaigning. We only need to look to Kenya's election history to understand why it is important. The 2007/2008 election resulted in violence that killed over 1,000 people and displaced over 600,000. The 2013 election was relatively peaceful, but marked the rise of online "hate speech" that exploited ethnic tensions. The 2017 election result was annulled and rerun amidst great tension and a death toll of at least 33 people, while targeted online political adverts played on national fears of further violence. The essay concludes with an outline of the expected minimum protections and safeguards, which can be applied globally. [ABSTRACT FROM AUTHOR]

Finger vein biometric trait has been increasingly used for personal verification or identification in security applications. Normally, the sample image of finger vein is captured under uneven illumination due to geometry variation of finger and it is usually in low contrast condition which will affect the extracted pattern of the vein. In order to have a reliable vein pattern extraction, the contrast and sharpness enhancement of finger image is executed before the pattern extraction procedure. Un-sharp Mask technique has been considered as one of good tools for the sharpness and contrast enhancement of images. However, it suffers with two drawbacks for our intended purpose which are halo effect appears around the finger image and over enhancement of noise to the vein image. Due to this problem, this paper proposes a Modified Un-sharp Mask (MUM) with Log-Gabor filter method to enhance the sharpness and contrast of finger vein image. In this study, the Modified Repeated Line Tracking (MRLT) algorithm is then used to extract the features from the enhanced vein pattern. The experimental results show that the extracted pattern of finger vein contains more details of vein and detection of small details of finger vascular is facilitated after applying the proposed finger vein enhancement method. [ABSTRACT FROM AUTHOR]

Internet of Things (IOT) is a newly developed concept in the world of technology and communication which provides the ability to transfer technological information to everything, including human, animals, or objects, through communication networks such as internet or intranet. Biometric technology offers various applications. The main objective is to provide an appropriate alternative for control systems with traditional access. It is also utilized for personal protection or corporate finance. Simultaneous application of Biometric technology and Fast Identity Online (FIDO) may advance interaction with objects and authentication of those who want to access crucial information. Furthermore, the authentication of individuals is a more accurate and faster process. This process is much more costly and time-consuming. This article presents a new approach for utilizing biometric technology in IOT, in order to manage biometric in IOT. The results demonstrate that fingerprint biometry obtains the highest priority as compared to other biometric techniques used in IOT, while it imposes the lowest implementation costs. Moreover, it provides a high level of security for the system management. [ABSTRACT FROM AUTHOR]

Biometric systems performing identity recognition based upon extracted informative data from an individual are vital for security applications. The vital characteristics of an ECG signal depend upon its Characteristic points’ P, Q, R, S and T. In this paper, an effective feature extraction method is proposed, in which for each record of ECG, the best 6-PQRST fragments are extracted according to priority basis and their positions are normalized. A total of 72 different features are calculated, finally the performance of feature set is examined and compared using ANN. The proposed algorithm is tested for MIT-BIH ECG ID database signals. [ABSTRACT FROM AUTHOR]

Rapid technological development has far-reaching implications for every aspect of life, including transportation. The emerging Intelligent Transportation System (ITS) based on the Internet of Vehicles (IoV) requires robust security measures due to the public channels used to communicate between vehicles and trusted authorities. These channels create opportunities for adversaries to intercept and exploit the transmitted data, leading to severe attacks. Moreover, cars are often parked in public areas, making them vulnerable to physical attacks such as theft of smart cards/devices, cloning, and impersonation. In this paper, we propose a new protocol for IoV that addresses these security concerns. Our protocol offers user authentication based on biometrics, using a biometric key to prevent revealing data from theft of smart cards/devices. Additionally, we use The Physical Unclonable Function (PUF) to resist cloning attacks. We have conducted an informal analysis, which confirms that our protocol fulfills security features. Furthermore, we have conducted a formal analysis using the RoR model and the Scyther tool to verify that our protocol achieves security properties and withstands well-known attacks. We also demonstrate the time complexity and show that our protocol has low computation time. Therefore, our protocol is secure and has a low computation time. [ABSTRACT FROM AUTHOR]

[Display omitted] • Provides an accurate personal authentication system based on EEG signals. • Generate stable cryptographic keys from multi-channel EEG signals. • Generate a set of cryptographic keys with different lengths from the same EEG signal. • Each generated key is unique and repeatable. • The generated keys passed the NIST, the scale index, and the autocorrelation tests. • Time complexity analysis of the key generation process is performed. • Key generation process is linear and fast enough for practical applications. Brain signals have recently been proposed as a strong biometric due to their characteristics such as, uniqueness, permanence, universality, and confidentiality. There are many factors that affect the stability of EEG signals as a biometric for example, using different recording devices, variation in participant emotional states, performing different mental tasks and recording in temporally spaced sessions. Due to the non-stationary nature of EEG signals, there are still speculations about the stability of using them for generating a unique and repeatable cryptographic key. The challenge that faces all biometric based crypto-systems is to overcome the variation in biometric itself over time and to generate multiple unique keys from the same observation. In this work, we investigate the stability of using EEG signals as a biometric for both personal authentication and cryptographic key generation. The authentication process was tested using three datasets AMIGOS, DEAP, and SEED. Achieving accuracy of 96.23 % , 98.85 % , and 99.89 % respectively. The key generation process generates a set of different keys with different lengths from the same observation. Each key is unique and repeatable. The generated keys were examined using NIST test suite, scale index test, and autocorrelation test. Time complexity analysis of the key generation process was performed. [ABSTRACT FROM AUTHOR]

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Fast IDentity Online(FIDO) supports biometric authentications in an online environment without transmitting biometric templates over the network. For a given FIDO client, the "Fuzzy Vault" securely stores biometric templates, houses additional biometric templates, and unlocks private keys via biometrics. The Fuzzy Vault has been extensively researched and some vulnerabilities have been discovered, such as brute force, correlation, and key inversions attacks. In this paper, we propose a simple fingerprint Fuzzy Vault for FIDO clients. By using the FIDO feature, a simple minutiae alignment, and point-to-point matching, our Fuzzy Vault provides a secure algorithm to combat a variety of attacks, such as brute force, correlation, and key inversions. Using a case study, we verified our Fuzzy Vault by using a publicly available fingerprint database. The results of our experiments show that the Genuine Acceptance Rate and the False Acceptance Rate range from 48.89% to 80% and from 0.02% to 0%, respectively. In addition, our Fuzzy Vault, compared to existing similar technologies, needed fewer attempts. [ABSTRACT FROM AUTHOR]

Computational Grids is highly heterogeneous shared resources for problem solving in any dynamic environment. Accepting Grid computing technologies will be difficult, unless users are certain of safety of their data like in their own environment. Security in Computational Grids is in two folds. Security of the grid users ensuring authentication, confidentiality, integrity, single sign on and delegation on one hand and security of the grid resources in the form of authorization and access control on the other hand. Existing methods of authentication in computational grids have proved inadequate for identifying users; hence, a more reliable technique is required. This paper provides a model, which allows users reliable transactions in grid by using fingerprint to enhance security in grid. Thus, this study aimed at hybridizing fingerprint biometric and Attribute Based Access Control (ABAC) for authenticating and authorizing computational grid users based on attributes of the users for computational grid resources. [ABSTRACT FROM AUTHOR]

Human Iris pattern matching and recognition system is considered to be the best biometric identification found so far because of the unique features found in the iris and moreover the textured patterns of iris remain stable, invariant and distinct throughout the whole life. Iris recognition techniques involve a mathematical analysis of the unique stable patterns that are structured within the iris and then the comparison is being done with an already existing database. In this paper the implementation of creating a fraud free voter ID list is being done as to make a clean Electoral environment. For this localization of Iris and Pupils are done by canny edge detection algorithm, Normalization is done by Dougman's Normalization method and feature extraction is being done using Log Gabor Filter and lastly method of matching is accomplished by Euclidian distance 1, 2 . MATLAB 2011 version is used for developing the present study, and much of the emphasis is given on software for Recognition of Irises in an efficient manner. [ABSTRACT FROM AUTHOR]

Phylogenetic comparison for lessepsian puffer fish Lagocephalus sceleratus (Gmelin, 1789) from the Egyptian waters (Mediterranean and Red Seas) was performed using morphological and genetic characteristics to confirm its identification and to recognize any variations due to differences in both habitats. A similar identity in body description from both habitats was observed. Slight phenotyping variations in some morphological measurements were observed with no significant difference. The correlation between the reference length and most of morphological indices was similar (0.99) for both habitats. Molecular phylogeny, using two primers (16S rRNA and Cytochrome b) showed similar size bands at 600 bp and 400 bp ladders for both primers respectively in both habitats. The obtained sequences were aligned showing an identity (more than 99%) to the published sequences of L. sceleratus indicating high genetic resemblance. So, the present populations from both habitats don’t appear to be separated species, indicating no obvious genetic variations after its migration into new habitat, expecting that, genetic differentiation may occur very slowly. This supported the idea that both morphological and genetic characteristics are recommended to be used together in identification and phylogenetic relations of the population from two habitats. The specific primers 16S rRNA and Cyt b showed confirmable tools to support the same target where the environmental variations may alter phenotypic variations. [ABSTRACT FROM AUTHOR]

The bilinear pairing, also known as Weil pairing or Tate pairing, is widely used in cryptography and its properties help to construct cryptographic schemes for different applications in which the security of the transmitted data is a major concern. In remote login authentication schemes, there are two major requirements: i) proving the identity of a user and the server for legitimacy without exposing their private keys and ii) freedom for a user to choose and change his password (private key) efficiently. Most of the existing methods based on the bilinear property have some security breaches due to the lack of features and the design issues. In this paper, we develop a new scheme using the bilinear property of an elliptic point and the biometric characteristics. Our method provides many features along with three major goals. a) Checking the correctness of the password before sending the authentication message, which prevents the wastage of communication cost; b) Efficient password change phase in which the user is asked to give a new password after checking the correctness of the current password without involving the server; c) User anonymity - enforcing the suitability of our scheme for applications in which a user does not want to disclose his identity. We use BAN logic to ensure the mutual authentication and session key agreement properties. The paper provides informal security analysis to illustrate that our scheme resists all the security attacks. Furthermore, we use the AVISPA tool for formal security verification of our scheme. [ABSTRACT FROM AUTHOR]

In this paper we investigate whether human digital fingerprints can be used to estimate human age-groups. To our knowledge, human age-group estimation using digital fingerprints have not been addressed formally. Human age-group estimation can be applied in the areas of online child protection, age based access control or customized services based on estimated age-groups. Motivated by the fact that human digital fingerprint vary in width ranging from birth to adulthood but pattern remains the same, we have developed a procedure to extract discriminating features using Curvelet Transform to classify fingerprints into three age groups. Experimental results show the feasibility of our method which can be used to protect children over cyberspace by automatically customizing their access according to their age-group. [ABSTRACT FROM AUTHOR]

The health of the forest seedlings can be translated also through growth vigor, and this directly affects the quality of stands regeneration and the production costs of the afforestation material. Mycorrhiza is a symbiotic association (mycorrhizal association), of bio system type, mutually beneficial (bilateral positive), made up of the mycelium of a fungus and the roots of herbaceous or woody plants. In trees, the relationship between the fungus and the plant is particularly important; many woody species cannot exist in the absence of mycorrhiza. The aim of the research is to find effective methods of inducing and stimulating ecto mycorrhiza in forestry nurseries and to determine their influence on biometric features of the oak seedlings, growth vigor and health status, in the early years of life, respectively. [ABSTRACT FROM AUTHOR]

The article discusses a study which compares four field methods for sexing Wedge-tailed shearwaters on Muttonbird Island, New South Wales, including cloaca inspection, biometrics, and acoustics. Findings show the percentage of efficiency of each method including 86% for cloacal sexing, 81% for biometrics, and 97% for acoustic discriminant function.

J Med Internet Res 2022;24(11):e38650 doi:10.2196/38650 [ABSTRACT FROM AUTHOR]