Face-Based Multiple User Active Authentication on Mobile Devices

Perera, Pramuditha; Patel, Vishal M.

doi:10.1109/tifs.2018.2876748

Cited by 44 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finger scan is the most popular biometric being used in mobile phones. In recent times face recognition in mobile [11], [12] is fast becoming popular. But all these methods come at an increased cost.…”

Section: Introductionmentioning

confidence: 99%

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

et al. 2020

View full text Add to dashboard Cite

The use of keystroke dynamics for user authentication has evolved over the years and has found its application in mobile phones. But the primary challenge with mobile phones is that they can be used in any position. Thus, it becomes critical to analyze the use of keystroke dynamics using the data collected in various typing positions. This research proposed a three-step authentication model that could be used to authenticate a user who is using the mobile in sitting, walking, and relaxing position. Furthermore, the mobile orientation (portrait and landscape) was considered while taking input from the user. Apart from using traditional keystroke features, accelerometer data were also combined for classification using Random Forest(RF) and K-Nearest Neighbour(KNN) classifiers. The three-step authentication method was able to authenticate a user with an EER of 2.9% for the relaxing landscape position. Finally, the model was optimized using Particle Swarm Optimization (PSO) to reduce the feature set and make the model more practical for mobile phones. Optimization helped to reduce the number of features from 55 to 17 and improved the EER to 2.2%. The research validated that relaxing and walking positions are the best positions to authenticate a user using keystroke dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In contrast, the non-linear kernel does not create straight boundaries, which implies that the non-linear algorithms utilize kernels to classify non-separable data into separable data. Many continuous authentication approaches utilize SVM for classification [ 10 , 11 , 12 , 13 ]. SVM can perform well on small datasets; however, like k -NN, it cannot perform well on large and noisy datasets [ 14 ].…”

Section: Preliminariesmentioning

confidence: 99%

Security, Privacy, and Usability in Continuous Authentication: A Survey

Baig

Eskeland

2021

Sensors

View full text Add to dashboard Cite

Continuous authentication has been proposed as a possible approach for passive and seamless user authentication, using sensor data comprising biometric, behavioral, and context-oriented characteristics. Since these are personal data being transmitted and are outside the control of the user, this approach causes privacy issues. Continuous authentication has security challenges concerning poor matching rates and susceptibility of replay attacks. The security issues are mainly poor matching rates and the problems of replay attacks. In this survey, we present an overview of continuous authentication and comprehensively discusses its different modes, and issues that these modes have related to security, privacy, and usability. A comparison of privacy-preserving approaches dealing with the privacy issues is provided, and lastly recommendations for secure, privacy-preserving, and user-friendly continuous authentication.

show abstract

“…The work presented in [ 31 ] focused on a face recognition algorithm for multiple users. This is a complex task, given that the accuracy behaves inversely to the number of individuals registered.…”

Section: Continuous Authenticationmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

View full text Add to dashboard Cite

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

show abstract

Face-Based Multiple User Active Authentication on Mobile Devices

Cited by 44 publications

References 22 publications

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Security, Privacy, and Usability in Continuous Authentication: A Survey

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Contact Info

Product

Resources

About