scholarly journals Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1568
Author(s):  
Junmo Kim ◽  
Geunbo Yang ◽  
Juhyeong Kim ◽  
Seungmin Lee ◽  
Ko Keun Kim ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Physical State ◽  
Acceptance Rate ◽  
Support Vector ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
Ecg Signals ◽  
Using Data ◽  
False Acceptance

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

scholarly journals Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

2018 ◽  
pp. 13-18 ◽  
Author(s):  
Didih Rizki Chandranegara ◽  
Fauzi Dwi Setiawan Sumadi
Keyword(s):  
Standard Deviation ◽  
High Accuracy ◽  
Previous Method ◽  
Acceptance Rate ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
False Acceptance ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

Authentication Based on Seating Pressure Distribution using the MT System

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
Shigeomi Koshimizu ◽  
Atsushi Koizumi
Keyword(s):  
Pressure Distribution ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
New Method ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
False Rejection Rate ◽  
Feature Values ◽  
False Acceptance ◽  
False Rejection

This paper proposes a system for authentication based on seating pressure distribution, using the MT system as a new method of biometric authentication that is difficult to forge and does not inconvenience users. The main characteristic is that the only action required of the user is to sit down. Feature values were extracted based on the pressure distribution when individuals seat, and individual users were distinguished from other persons by means of the Mahalanobis-Taguchi (MT) system used in quality engineering. The result of the experiment was a False Rejection Rate of 2.2% and a False Acceptance Rate of 1.1%. 

scholarly journals Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 124
Author(s):  
Uladzislau Barayeu ◽  
Nastassya Horlava ◽  
Arno Libert ◽  
Marc Van Hulle
Keyword(s):  
Binary Classification ◽  
Personal Data ◽  
Principal Component ◽  
Acceptance Rate ◽  
Support Vector ◽  
False Acceptance Rate ◽  
Movement Data ◽  
The Subject ◽  
Deep Learning Neural Network ◽  
False Acceptance

The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively.

Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi

2018 ◽  
Vol 35 ◽  
pp. 96-108
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Mohammad Iqbal Omar
Keyword(s):  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Features Extraction ◽  
Raspberry Pi ◽  
Support Vector ◽  
Ecg Signal ◽  
Statistical Features ◽  
Classification Approach ◽  
Ecg Signals ◽  
Heart Monitoring

Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patient's body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.

scholarly journals Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

2021 ◽  
Vol 17 (02) ◽  
pp. 106
Author(s):  
Mohand Lokman Ahmad Al-dabag ◽  
Haider Th. Salim ALRikabi ◽  
Raid Rafi Omar Al-Nima
Keyword(s):  
Atrial Fibrillation ◽  
Computational Cost ◽  
Extraction Methods ◽  
Support Vector ◽  
Ecg Signal ◽  
Heart Problem ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

scholarly journals A STATIC SIGNATURE VERIFICATION SYSTEM BASED ON A COOPERATING NEURAL NETWORKS ARCHITECTURE

1994 ◽  
Vol 08 (03) ◽  
pp. 679-692 ◽  
Author(s):  
HUBERT CARDOT ◽  
MARINETTE REVENU ◽  
BERNARD VICTORRI ◽  
MARIE-JOSÈPHE REVILLET
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Signature Verification ◽  
Geometrical Parameters ◽  
False Acceptance Rate ◽  
Static Information ◽  
Handwritten Signature ◽  
False Acceptance

We are applying neural networks to the problem of handwritten signature verification. Our system is working on checks, so we can only use the static information (the image). This static information is used in three representations: geometrical parameters, outline and image. Our system is composed of several neural networks which cooperate together during the learning and decision phases. The performances in generalization, obtained with a large-scale database of 6000 signatures from real checks on random forgeries, are False Acceptance Rate (FAR)=2% and False Rejection Rate (FRR)=4%.

Distance Bounding Protocol with Adjustable False Acceptance Rate

2011 ◽  
Vol 15 (4) ◽  
pp. 434-436 ◽  
Author(s):  
Dae Hyun Yum ◽  
Jin Seok Kim ◽  
Sung Je Hong ◽  
Pil Joong Lee
Keyword(s):  
Acceptance Rate ◽  
False Acceptance Rate ◽  
Distance Bounding Protocol ◽  
False Acceptance

scholarly journals Classification of Heart Rate Time Series Using Machine Learning Algorithms

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Heart Patients ◽  
Extraction Step ◽  
Ecg Arrhythmia ◽  
Electrocardiogram Ecg ◽  
The Given

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

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