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%. 

COMPARISON OF COLOR AND TEXTURE FOR IRIS RECOGNITION

2012 ◽  
Vol 26 (03) ◽  
pp. 1256007 ◽  
Author(s):  
LENINA BIRGALE ◽  
MANESH KOKARE
Keyword(s):  
Iris Recognition ◽  
Feature Vector ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Computational Time ◽  
False Acceptance Rate ◽  
Recognition Systems ◽  
Normalization Process ◽  
False Acceptance ◽  
False Rejection

This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 × 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time efficiency achieved is of 947.7 ms.

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.

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%.

VPID: Towards Vein Pattern Identification Using Thermal Imaging

i-com ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. 259-270
Author(s):  
Sarah Faltaous ◽  
Jonathan Liebers ◽  
Yomna Abdelrahman ◽  
Florian Alt ◽  
Stefan Schneegass
Keyword(s):  
Thermal Imaging ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Identification System ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
Vein Pattern ◽  
Implicit Approach ◽  
Distance Difference

AbstractBiometric authentication received considerable attention lately. The vein pattern on the back of the hand is a unique biometric that can be measured through thermal imaging. Detecting this pattern provides an implicit approach that can authenticate users while interacting. In this paper, we present the Vein-Identification system, called VPID. It consists of a vein pattern recognition pipeline and an authentication part. We implemented six different vein-based authentication approaches by combining thermal imaging and computer vision algorithms. Through a study, we show that the approaches achieve a low false-acceptance rate (“FAR”) and a low false-rejection rate (“FRR”). Our findings show that the best approach is the Hausdorff distance-difference applied in combination with a Convolutional Neural Networks (CNN) classification of stacked images.

HC2D Barcode for Handwritings Verification Using Embedded Camera on Tablet

2015 ◽  
Vol 752-753 ◽  
pp. 1069-1072
Author(s):  
Puchong Subpratatsavee ◽  
Wissawat Sakulsaknimit
Keyword(s):  
Credit Cards ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Biometric Identification ◽  
False Acceptance Rate ◽  
Person Identification ◽  
Biometric Verification ◽  
Short Time ◽  
Financial Transactions ◽  
False Acceptance

The authentication of a person is used to determine the stage of the transaction, such as a password or authentication by biometric verification. However, the majority such as financial transactions, and communications sector confirmed the use of credit cards and checks your ID card at all. Biometric identification method fingerprints, photographs, and signatures, especially the signature is the most popular. It is simple and easy to use. The signature can be easily copied in a short time because it does not require any special tools or equipment tampering. This paper presents the person identification using handwritten signatures is based on the detection of movement and HC2D Barcode false acceptance rate and false rejection rate is 0 and 0.2.

scholarly journals Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

2020 ◽  
Author(s):  
Yong Wang ◽  
Zhuoyi Su ◽  
Zhengyu Zhu
Keyword(s):  
State Of The Art ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
False Acceptance Rate ◽  
Convolutional Network ◽  
Average Accuracy ◽  
Common Operation ◽  
Identification Model ◽  
State Of Art ◽  
Target Speaker

Abstract Nowadays, speaker disguise is a common operation that presents a great challenge to social security. Therefore, it is important to recognize the authenticity of speech. Most of the current researches focus on speech spoofing, which simulates a target speaker to break through the state-of-art ASV systems by increasing false acceptance rate. Meanwhile, there is another type of disguise, i.e. de-identification, which transforms a speech signal without a target to increase the false rejection rate in order not to be recognized. It has received far less attention. Therefore, in this paper, we investigate the de-identification model and propose a method to detect de-identification speeches from genuine speeches by using a very deep dense convolutional network with 135 layers. The experimental results show that the average accuracy of the proposed method outperforms the reported state-of-the-art methods.

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