User Authentication for Mobile Devices

Rogowski, Marcin; Saeed, Khalid; Rybnik, Mariusz; Tabędzki, Marek; Adamski, Marcin

doi:10.1007/978-3-642-40925-7_5

Cited by 13 publications

(7 citation statements)

References 10 publications

(11 reference statements)

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“…• Vocal fold (V): Voice produced by vocal folds (cords) is a natural interaction mechanism that we use on a daily basis. Advances in speech recognition technology have made it possible to use voice as a human-computer interface and also as a speaker recognition mechanism [51].…”

Section: Actuatormentioning

confidence: 99%

“…In this interaction, a user is typically authenticated by speaking into a microphone embedded in a device. Voice authentication, also known as speaker recognition, is broadly classified into two types: text-independent and text-dependent [51]. Whisper authentication is a variation of text-independent speaker recognition that aims to enhance acceptability and usability of the system [150].…”

Section: M-*-* Schemes: Microphone-based Au-thenticationmentioning

confidence: 99%

See 1 more Smart Citation

Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey

Sae-Bae

Memon

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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As the convenience and cost benefits of Natural User Interface (NUI) technologies are hastening their wide adoption, computing devices equipped with such interfaces are becoming ubiquitous. Used for a broad range of applications, from accessing email and bank accounts to home automation and interacting with a healthcare provider, such devices require, more than ever before, a secure yet convenient user authentication mechanism. This paper introduces a new taxonomy and presents a survey of "point-of-entry" user-device authentication mechanisms that employ a natural user interaction. The taxonomy allows a grouping of the surveyed techniques based on the sensor type used to capture user input, the actuator a user applies during interaction, and the credential type used for authentication. A set of security and usability evaluation criteria are then proposed based on the Bonneau, Herley, Van Oorschot and Stajano framework. An analysis of a selection of techniques and, more importantly, the broader taxonomy elements they belong to, based on these evaluation criteria, are provided. This analysis and taxonomy provide a framework for the comparison of different authentication alternatives given an application and a targeted threat model. Similarly, the taxonomy and analysis also offer insights into possibly unexplored, yet potentially rewarding, research avenues for NUI-based user authentication that could be explored.

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Section: Actuatormentioning

confidence: 99%

Section: M-*-* Schemes: Microphone-based Au-thenticationmentioning

confidence: 99%

Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey

Sae-Bae

Memon

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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show abstract

“…A gyroscope detects the current orientation of the device and any possible spin or rotational change. Accelerometers and gyroscopes always return threedimensional values [13]. The orientation of the smartphone can be computed from the angular velocity detected by the gyroscope, which is expressed on 3 axes…”

Section: E Smartphone Sensorsmentioning

confidence: 99%

“…Motion sensors measure acceleration and rotational forces along three axes. Examples of such sensors are accelerometers, gravity sensors, gyroscopes and rotational vector sensors [13]. An accelerometer can measure any movement of the phone including fall of the owner when holding the phone or free fall of the phone.…”

Section: E Smartphone Sensorsmentioning

confidence: 99%

Authentication of Smartphone Users Using Behavioral Biometrics

Alzubaidi

Kalita

2016

IEEE Commun. Surv. Tutorials

162

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Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work and socialize whenever and wherever they want. Smartphones are small in size; so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.

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“…For person authentication, passwords, PIN codes, or patterns [1][2][3] have been used in smartphones. However, these require users to remember them.…”

Section: Introductionmentioning

confidence: 99%

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

Takahashi

Masegi

Nakanishi

2021

HCI International 2021 - Posters

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Writer verification based on finger-writing of a simple symbol on a touch screen is proposed herein. The users write a simple and well-known symbol, for example, a circle, triangle, or square. In addition, the users write the symbol using their finger instead of a pen on a tablet. This allows more convenience with the use of the proposed method. However, it was observed that the original approach and obtained verification performance were not reliable. In this work, we create a new database using thirty participants. By examining individual features extracted from the database, the risk of misjudgment is found. In order to solve this problem, a coordinate transformation method is introduced. Moreover, normalization is examined for fusing individual features by comparing three normalization methods. The proposed method with appropriate coordinate transformation and normalization achieves an equal error rate of 10.6% even when all participants write only a simple circle.

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User Authentication for Mobile Devices

Cited by 13 publications

References 10 publications

Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey

Emerging NUI-Based Methods for User Authentication: A New Taxonomy and Survey

Authentication of Smartphone Users Using Behavioral Biometrics

A Smartphone User Verification Method Based on Finger-Writing of a Simple Symbol

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