A Leap Password based verification system

Chahar, Aman; Yadav, Shivangi; Nigam, Ishan; Singh, Richa; Vatsa, Mayank

doi:10.1109/btas.2015.7358745

Cited by 24 publications

(15 citation statements)

References 6 publications

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“…Yang et al [ 25 ] invited 110 participants for performing the experiment, which is because their research is a comparison between text password and gesture password, then they got a result that using gesture password could get 98.90% at most. Chahar et al [ 11 ] created spoofing samples by 3 individuals and then they got the result 1% FAR on 18.83% FRR to support their conditional mutual information maximization method. Sun et al [ 26 ] have some similarity to our methods, they also made data smoothing and data normalization, but they used a DTW algorithm to make sequence alignment.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…We have considered several input devices for gesture information collection, such as the devices used in previous research [ 7 , 8 , 9 , 10 , 11 ]. There are two primary reasons for choosing this device.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, authentication can be considered as anomaly detection, and anomaly detection is an OCC problem. Unlike previous studies [ 7 , 8 , 9 , 10 , 11 ] in gesture authentication, a system based on OCC could be more accurately called “authentication” because it can directly identify the user without the need to rely on other user groups. Gesture authentication is mostly multi-class classification in the previous studies, which means the system can only distinguish which user is the “authorized user,” but it cannot directly identify the user without other information.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [ 17 ] also used Leap Motion with a hidden Markov model as the classification method for multi-class classification. Aman Chahar et al [ 11 ] proposed a system also based on Leap Motion, using a conditional mutual information maximization algorithm to select the optimal feature set. Match-score fusion was performed to reconcile information from multiple classifiers.…”

Section: Introductionmentioning

confidence: 99%

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GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification

Wang

Tanaka

2018

Sensors

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Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and we use incremental learning technology for allowing the system to memorize the gesture incrementally over time. An experiment has been performed on different gestures in different user situations that demonstrates the accuracy of one-class classification (OCC), and proves the effectiveness and reliability of the approach. Gesture authentication based on 3D depth cameras could be achieved with reduced user effort.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification

Wang

Tanaka

2018

Sensors

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“…One such scheme is the Leap Password. Using the Leap Motion sensor [41], Chahar et al [113] proposed to authenticate a user from six in-air one-finger taps performed in front of the device. A set of features are extracted from each of the samples, including the dimensions of the palms and fingers of a user's hand, the timing of the tap sequence, and the motion.…”

Section: C-f-x Schemes: Finger-motion Techniquesmentioning

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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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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Hand Gesture Authentication Using Depth Camera

Zhao

Tanaka

2018

Advances in Intelligent Systems and Computing

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A Leap Password based verification system

Cited by 24 publications

References 6 publications

GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification

GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification

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

Hand Gesture Authentication Using Depth Camera

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