scholarly journals Multi-Layer Feature Based Shoeprint Verification Algorithm for Camera Sensor Images

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2491
Author(s):  
Xinnian Wang ◽  
Yanjun Wu ◽  
Tao Zhang
Keyword(s):  
Error Rate ◽  
Feature Matching ◽  
Similarity Score ◽  
Challenging Problem ◽  
Equal Error Rate ◽  
Forensic Evidence ◽  
Dna Evidence ◽  
Verification Algorithm ◽  
Forensic Practice ◽  
Feature Based

As a kind of forensic evidence, shoeprints have been treated as important as fingerprint and DNA evidence in forensic investigations. Shoeprint verification is used to determine whether two shoeprints could, or could not, have been made by the same shoe. Successful shoeprint verification has tremendous evidentiary value, and the result can link a suspect to a crime, or even link crime scenes to each other. In forensic practice, shoeprint verification is manually performed by forensic experts; however, it is too dependent on experts’ experience. This is a meaningful and challenging problem, and there are few attempts to tackle it in the literatures. In this paper, we propose a multi-layer feature-based method to conduct shoeprint verification automatically. Firstly, we extracted multi-layer features; and then conducted multi-layer feature matching and calculated the total similarity score. Finally, we drew a verification conclusion according to the total similarity score. We conducted extensive experiments to evaluate the effectiveness of the proposed method on two shoeprint datasets. Experimental results showed that the proposed method achieved good performance with an equal error rate (EER) of 3.2% on the MUES-SV1KR2R dataset and an EER of 10.9% on the MUES-SV2HS2S dataset.

scholarly journals Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Margit Antal ◽  
László Zsolt Szabó ◽  
Tünde Tordai
Keyword(s):  
Error Rate ◽  
Error Rates ◽  
Equal Error Rate ◽  
Global Features ◽  
Evaluation Protocol ◽  
Global Threshold ◽  
Online Signature ◽  
Feature Based ◽  
Basic Quality

We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. The database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. The database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. The lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. The same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. The results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.

Misuse Detection for Mobile Devices Using Behaviour Profiling

2011 ◽  
Vol 1 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Fudong Li ◽  
Nathan Clarke ◽  
Maria Papadaki ◽  
Paul Dowland
Keyword(s):  
Mobile Devices ◽  
Error Rate ◽  
Text Message ◽  
Modern Society ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Misuse Detection ◽  
General Application ◽  
Point Of Entry ◽  
Application Specific

Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.

scholarly journals A User Authentication Enabled Piezoelectric Force Touch System for the Internet of Things

2020 ◽  
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Arokia Nathan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internet Of Things ◽  
Error Rate ◽  
User Authentication ◽  
The Internet ◽  
Equal Error Rate ◽  
High User ◽  
Iot Applications ◽  
The Internet Of Things

In Internet of Things (IoT) applications, among various authentication techniques, keystroke authentication methods based on a user’s touch behavior have received increasing attention, due to their unique benefits. In this paper, we present a technique for obtaining high user authentication accuracy by utilizing a user’s touch time and force information, which are obtained from an assembled piezoelectric touch panel. After combining artificial neural networks with the user’s touch features, an equal error rate (EER) of 1.09% is achieved, and hence advancing the development of security techniques in the field of IoT.

Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing

2022 ◽  
Vol 31 (2) ◽  
pp. 1-32
Author(s):  
Luca Ardito ◽  
Andrea Bottino ◽  
Riccardo Coppola ◽  
Fabrizio Lamberti ◽  
Francesco Manigrasso ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Matching ◽  
State Of The Art ◽  
Design Of Algorithms ◽  
Computational Burden ◽  
Domain Specific ◽  
Gui Testing ◽  
Wide Range ◽  
Full Screen ◽  
Feature Based

In automated Visual GUI Testing (VGT) for Android devices, the available tools often suffer from low robustness to mobile fragmentation, leading to incorrect results when running the same tests on different devices. To soften these issues, we evaluate two feature matching-based approaches for widget detection in VGT scripts, which use, respectively, the complete full-screen snapshot of the application ( Fullscreen ) and the cropped images of its widgets ( Cropped ) as visual locators to match on emulated devices. Our analysis includes validating the portability of different feature-based visual locators over various apps and devices and evaluating their robustness in terms of cross-device portability and correctly executed interactions. We assessed our results through a comparison with two state-of-the-art tools, EyeAutomate and Sikuli. Despite a limited increase in the computational burden, our Fullscreen approach outperformed state-of-the-art tools in terms of correctly identified locators across a wide range of devices and led to a 30% increase in passing tests. Our work shows that VGT tools’ dependability can be improved by bridging the testing and computer vision communities. This connection enables the design of algorithms targeted to domain-specific needs and thus inherently more usable and robust.

scholarly journals EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

2019 ◽  
Vol 11 (24) ◽  
pp. 3026
Author(s):  
Bin Fang ◽  
Kun Yu ◽  
Jie Ma ◽  
Pei An
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Maximum Clique ◽  
Feature Space ◽  
Multispectral Image ◽  
Feature Descriptor ◽  
Ranking Algorithm ◽  
Feature Based ◽  
Edge Feature

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

scholarly journals Implicit Secondary Authentication for Sustainable SMS Authentication

2019 ◽  
Vol 11 (1) ◽  
pp. 279
Author(s):  
Gwonsang Ryu ◽  
Seung-Hyun Kim ◽  
Daeseon Choi
Keyword(s):  
Error Rate ◽  
Access Point ◽  
Environmental Information ◽  
Behavioral Pattern ◽  
Environmental Data ◽  
Smart Device ◽  
Short Message ◽  
Equal Error Rate ◽  
Smart Watch ◽  
Message Service

Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.

Detecção de Anomalias em Vias Públicas Usando Características Espaciais e um Classificador Sequencial Bidirecional

2020 ◽  
Author(s):  
Fábio Ricardo Oliveira Bento ◽  
Raquel Frizera Vassallo ◽  
Jorge Leonid Aching Samatelo
Keyword(s):  
Anomaly Detection ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Error Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Equal Error Rate ◽  
Operating Characteristic Curve ◽  
End To End ◽  
Recall Curve

Detecção de anomalias consiste na identificação de eventos que não estão em conformidade com um padrão de comportamento esperado. No contexto de segurança em vias públicas, a detecção automática de eventos anômalos através de video, tem aplicação na identificação de comportamentos suspeitos. Nesse artigo é proposta uma abordagem para o problema da detecção automática de eventos anômalos em vı́deos de vias públicas baseado em um modelo de redes neurais profundas end-to-end, composto de duas partes: um extrator de caracterı́sticas espaciais baseado em uma rede neural convolucional pre-treinada, e um classificador de sequências temporais baseado em camadas recorrentes empilhadas. Realizamos experimentos nos conjuntos de dados UCSD Anomaly Detection Dataset. Os resultados foram avaliados com as métricas Area Under the Receiver Operating Characteristic Curve - AUC, Area Under the Precision vs Recall Curve - AUPRC e Equal Error Rate – EER. Durante os experimentos, o modelo obteve AUC acima de 95% e EER abaixo de 9%, os quais são resultados compatı́veis com a literatura atual.

Gold versus platinum: Do jurors recognize the superiority and limitations of DNA evidence compared to other types of forensic evidence?

2008 ◽  
Vol 14 (1) ◽  
pp. 27-62 ◽  
Author(s):  
Joel D. Lieberman ◽  
Courtney A. Carrell ◽  
Terance D. Miethe ◽  
Daniel A. Krauss
Keyword(s):  
Forensic Evidence ◽  
Dna Evidence

HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER

2007 ◽  
Vol 19 (06) ◽  
pp. 359-374 ◽  
Author(s):  
Yih-Chih Chiou ◽  
Chern-Sheng Lin ◽  
Cheng-Yu Lin
Keyword(s):  
Breast Cancer ◽  
Image Registration ◽  
Early Detection ◽  
Feature Matching ◽  
Automatic Detection ◽  
Spatial Transformation ◽  
Registration Method ◽  
Feature Based ◽  
Mammogram Images ◽  
Hybrid Registration

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.

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