Bi-modal biometric authentication on mobile phones in challenging conditions

Khoury, Elie; Shafey, Laurent El; McCool, Chris; Günther, Manuel; Marcel, Sébastien

doi:10.1016/j.imavis.2013.10.001

Cited by 31 publications

(25 citation statements)

References 36 publications

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“…Physiological biometric techniques are based on biological measurements and inherent characteristics that a person owns such as face, iris, teeth, voice, fingerprint, hand geometry, and heart rate [2,[6][7][8]. Behavioral biometric techniques are related to something that a person is able to perform and repeat in a unique manner, such as handwriting signature, gait, and keystroke dynamics [4,7,9,10].…”

Section: Biometric Authenticationmentioning

confidence: 99%

A 3-D Hand Gesture Signature Based Biometric Authentication System for Smartphones

Sun

Wang

et al. 2015

Proceedings of the 8th International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONETICS

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The authentication mechanism being equipped in most of the smartphones, by detecting a 4-digit password or a simple pattern, are easy to be hacked and impersonated. In this paper, a 3-D hand gesture signature (HGS) based biometric authentication system is designed and implemented by taking advantage of the on-phone accelerometer to capture the 3-D acceleration information when user makes a gesture to gain access to the phone. The captured data will be processed through a sequence of signal processing such as data smooth, gesture spotting, sequence alignment, and interpolation, and then a match rule will be used to compare the processed data and the genuine user's registered pattern to determine whether granting the access to the phone to the user. And an automatic template updating strategy based on cluster analysis is proposed to improve the stability of the system. The 3-D HGS authentication system has been implemented on real smartphones, and the results tested for a total of 76 520 times by 19 users show very low false acceptance (0.27%) and false rejection rates (4.65%). Furthermore, comparison tests have been carried out among the 3-D HGS and two similar authentication systems by exporting the real gesture samples from the phones to a desktop PC, the simulation results reveal the 3-D HGS system has the best authentication accuracy.

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Section: Biometric Authenticationmentioning

confidence: 99%

A 3-D Hand Gesture Signature Based Biometric Authentication System for Smartphones

Sun

Wang

et al. 2015

Proceedings of the 8th International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONETICS

View full text Add to dashboard Cite

show abstract

“…We believe that multi-modal frameworks are more likely to provide meaningful security guarantees. A combination of face recognition and speech [17], and of gait and voice [32] have been proposed in this context. Deep learning techniques, which achieved early success modeling sequential data such as motion capture [31] and video [16] have shown promise in multi-modal feature learning [22], [30], [15], [21].…”

Section: Related Workmentioning

confidence: 99%

Learning Human Identity From Motion Patterns

Neverova¹,

Wolf²,

Lacey³

et al. 2016

IEEE Access

155

110

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We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind data set of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We compare several neural architectures for efficient learning of temporal multi-modal data representations, propose an optimized shift-invariant dense convolutional mechanism, and incorporate the discriminatively trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems. Finally, we demonstrate that the proposed model can also be successfully applied in a visual context.

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“…ISV and Graphs require resolutions that are a bit higher, but also these algorithms settle around 100 pixels image height. Since there is not much difference between the resolutions greater than 32 × 40 pixels, we choose to stick at the resolution 64 × 80 as used in many of our previous publications [14,23,68,73] for the rest of our experiments.…”

Section: Configuration Optimizationmentioning

confidence: 99%

Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Günther

Shafey

Marcel

2016

Face Recognition Across the Imaging Spectrum

Self Cite

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One important type of biometric authentication is face recognition, a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted towards unconstrained "in-the-wild" still images and videos. To some extent, current state-of-the-art systems are able to cope with variability due to pose, illumination, expression, and size, which represent the challenges in unconstrained face recognition. To date, only few attempts have addressed the problem of face recognition in mobile environment, where high degradation is present during both data acquisition and transmission. This book chapter deals with face recognition in mobile and other challenging environments, where both still images and video sequences are examined. We provide an experimental study of one commercial of-the-shelf and four recent open-source face recognition algorithms, including color-based linear discriminant analysis, local Gabor binary pattern histogram sequences, Gabor grid graphs and inter-session variability modeling. Experiments are performed on several freely available challenging still image and video face databases, including one mobile database, always following the evaluation protocols that are attached to the databases. Finally, we supply an easily extensible opensource toolbox to re-run all the experiments, which includes the modeling techniques, the evaluation protocols and metrics used in the experiments, and provides a detailed description on how to re-generate the results.

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Bi-modal biometric authentication on mobile phones in challenging conditions

Cited by 31 publications

References 36 publications

A 3-D Hand Gesture Signature Based Biometric Authentication System for Smartphones

A 3-D Hand Gesture Signature Based Biometric Authentication System for Smartphones

Learning Human Identity From Motion Patterns

Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

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