Biometric Authentication Based on Touchscreen Swipe Patterns

Antal, Margit; Szabó, László Z.

doi:10.1016/j.protcy.2016.01.061

Cited by 42 publications

(31 citation statements)

References 10 publications

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“…Similarly, in [20] a touch-gesture continuous authentication scheme is produced but limited to swipe-based gestures. In [2] and [1], touch-gestures are used to authenticate but gestures are limited to swipes in a non-continuous architecture. Authors of [10] benchmark their touch-gesture authentication scheme against the state-of-the-art by using multiple touch-gesture datasets.…”

Section: Related Workmentioning

confidence: 99%

A novel word-independent gesture-typing continuous authentication scheme for mobile devices

Smith-Creasey

Rajarajan

2019

Computers & Security

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In this study we produce a new continuous authentication scheme for gesture-typing on mobile devices. Our scheme is the first scheme that authenticates gesture-typing interactions in a word-agnostic format. The scheme relies on groupings of features extracted from the word gesture after it has been reduced to parts common to all gestures. We show that movement sensors are also important in differentiating between users. We describe the feature extraction processes and analyse our proposed feature set. The unique process of our authentication scheme is presented and described. We collect our own gesture typing dataset including data collected during sitting, standing and walking activities for realism. We test our features against state-of-the-art touch-screen interaction features and compare feature extraction times on real mobile devices. Our scheme authenticates users with an equal error rate of 3.58% for a single word-gesture. The equal error rate is reduced to 0.81% when 3 word-gestures are used to authenticate.

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Section: Related Workmentioning

confidence: 99%

A novel word-independent gesture-typing continuous authentication scheme for mobile devices

Smith-Creasey

Rajarajan

2019

Computers & Security

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“…Examples include face recognition [12], typist recognition [9], smart-stroke [8], touch [5], and mouse dynamics [16]. Antal et al [5] used four OCC that included Parzen density estimator, the k nearest-neighbor (kNN), Gaussian mixtures method and Support Vector Data Description method in order to build authentication systems based on swipe gestures, however, it was unclear whether their authentication framework was one-time or continuous. Moreover, the swipe gestures and micro-movements of the device were collected in a constrained environment under a very specific scenario -while responding to psychological questionnaire.…”

Section: Related Workmentioning

confidence: 99%

“…The disadvantages of using M CC is that they require samples from both genuine and impostor classes to determine respective decision boundaries. In other words, to build an M CC-based biometric system, both genuine and impostor samples must be collected [5]. For training and testing the classification models, researchers have conventionally used samples from other users than the genuine one as impostors [1,6,7].…”

Section: Introductionmentioning

confidence: 99%

Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances.Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.

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“…Behavioral biometrics use a user's operational habits and preferences as identify information, and hand gesture is a typical example investigated for authentication to capture biometric information on many smartphone platforms. Many researchers tried to distinguish users based on their input patterns and touch characteristics by extracting information from touch movements on smartphones [18][19][20][21][22][23]. These studies concluded that it is possible to positively improve the security of password patterns on touchscreens by integrating behavioral biometrics.…”

Section: Background and Related Studiesmentioning

confidence: 99%

Developing a New Haptic Passcode for User Authentication with a Personal ID Tag to Prevent Copying Attacks

2019

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We designed a haptic authentication system with a personal identification (ID) tag equipped with an embedded acceleration sensor and a radio frequency identification (RFID) tag that can be used in automated security systems. In the suggested system, users generate a passcode by haptic manipulation of an ID tag instead of typing in passwords using a keypad. An ID tag may be an ideal personalized gadget for ID authentication in many circumstances. The core of the proposed haptic authentication system is the activity-recognizable process that can be manipulated by the probabilistic model we developed and generate a much more secure passcode for authentication. With the system learning a total of 250 trials by 50 users for each haptic gesture, the average successful classification rate for the four-digit haptic passcode authentication was 88.9% using the probabilistic model we developed. Some haptic gestures, such as vertical upward flipping of the ID tag, showed lower accuracy than others due to the difficulty in haptic manipulation of the ID tag. From a copying attack experiment to examine the reliability of our haptic passcode scheme, it was proved that the haptic gestures with enough digits, apparently at least 6, were difficult enough to resist being copied by attackers. The same authentication scheme can also be applied for other personal devices that can be manipulated by hands for making haptic gestures, such as a mobile phone.

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Biometric Authentication Based on Touchscreen Swipe Patterns

Cited by 42 publications

References 10 publications

A novel word-independent gesture-typing continuous authentication scheme for mobile devices

A novel word-independent gesture-typing continuous authentication scheme for mobile devices

Continuous authentication using one-class classifiers and their fusion

Developing a New Haptic Passcode for User Authentication with a Personal ID Tag to Prevent Copying Attacks

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