Smartphone User Identity Verification Using Gait Characteristics

Damaševičius, Robertas; Maskeliūnas, Rytis; Venčkauskas, Algimantas; Woźniak, Marcin

doi:10.3390/sym8100100

Cited by 66 publications

(32 citation statements)

References 48 publications

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“…For example, age has minimal impact on features of the ear, and it can be a very convenient source of data for passive biometrics, but continuous authentication of the ear is difficult [28], [29]. Robertas et al [30] discussed gait (a person's walking pattern) based biometric feature which can be, unobtrusive, implicit, and passively observed continuously, so long as the user is carrying her/ his smartphone or is around it, though gait can be affected by many factors such as shoes, stimulants, mood, aging, etc.…”

Section: Related Workmentioning

confidence: 99%

A mobile app to make biometrics usage more secure

Singh¹,

Singh²

2020

Preprint

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With high paced growth in biometrics, its easy availability to capture various biometric features, it is <a>emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. </a>Organizations encourage their members to use biometrics, but <a>they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. </a>We propose a solution of an extra layer of security, via a low-cost mobile app framework, “Bio-Guard,” to use biometrics, more securely. We tested the app for its design, functions, usability, and it got a score of more than 71% on the usability scale and a reasonably low equal error rate (EER) of 6%. The survey to evaluate the usefulness of the app showed a favorable response of 80%. The results show a good potential of the app to make access to biometric data more secure. The app may enhance users’ confidence-level in encouraging higher participation of users in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many medium and small segment organizations to decrease their information security risk.

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Section: Related Workmentioning

confidence: 99%

A mobile app to make biometrics usage more secure

Singh¹,

Singh²

2020

Preprint

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“…walking style of a user under different conditions. (Damaševičius et al, 2016;Fernandez-Lopez et al, 2016) utilized smartphone internal motion sensors for validating users based on gait characteristics. (San-Segundo et al, 2016) used smartphone inertial sensors to develop a Gait-based Person Identification (GPI) scheme based on a Gaussian Mixture Model-Universal Background Model (GMM-UBM).…”

Section: Background and Related Workmentioning

confidence: 99%

Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing

Ehatisham-ul-Haq

Azam

Naeem

et al. 2018

Journal of Network and Computer Applications

115

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Smartphones are inescapable devices, which are becoming more and more intelligent and context-aware with emerging sensing, networking and computing capabilities. They offer a captivating platform to the users for performing a wide variety of tasks including socializing, communication, sending or receiving emails, storing and accessing personal data etc. at anytime and anywhere. Nowadays, loads of people tend to store different types of private and sensitive data in their smartphones including bank account details, personal identifiers, accounts credentials, and credit card details. A lot of people keep their personal e-accounts logged in all the time in their mobile devices. Hence these mobile devices are prone to different security and privacy threats and attacks from the attackers. Commonly used approaches for securing mobile devices such as passcode, PINs, pattern lock, face recognition, and fingerprint scan are vulnerable and exposed to several attacks including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To address these challenges, a novel continuous authentication scheme is presented in this study, which recognizes smartphone users on the basis of their physical activity patterns using accelerometer, gyroscope, and magnetometer sensors of smartphone. A series of experiments are performed for user recognition using different machine learning classifiers, where six different activities are analyzed for the multiple locations of smartphone on the user's body. SVM classifier achieved the best results for user recognition with an overall average accuracy of 97.95%. A comprehensive analysis of the user recognition results validates the efficiency of the proposed scheme.

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“…The most popular are artificial neural networks which are inspired by the mechanisms occurring in the brain. An important element of scientific research is the improvement and design of new solutions that may later be used in biometrics [1]- [6]. One of the last achievements in this field are papers on the interpretation of the signature in numerical form and the use of so-processed samples in the training process [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Signature analysis system using a convolutional neural network

Winnicka

Kęsik

Połap

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Identity verification using biometric methods has been used for many years. A special case is a handwritten signature made on a digital device or piece of paper. For the digital analysis and verification of its authenticity, special methods are needed. Unfortunately, this is a rather complicated task that quite often requires complex processing techniques. In this paper, we propose a system of signatures verification consisting of two stages. In the first one, a signature pattern is created. Thanks to this, the first attempt to verify identity takes place. In the case of approval, the second stage is followed by the processing of a graphic sample containing a signature by the convolutional neural network. The proposed technique has been described, tested and discussed due to its practical use.

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Smartphone User Identity Verification Using Gait Characteristics

Cited by 66 publications

References 48 publications

A mobile app to make biometrics usage more secure

A mobile app to make biometrics usage more secure

Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing

Signature analysis system using a convolutional neural network

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