Liveness Detection in Biometrics

Rogmann, Nils; Krieg, Maximilian

doi:10.1109/biosig.2015.7314611

Cited by 13 publications

(2 citation statements)

References 2 publications

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“…Liveness detection helps to distinguish between living and non-living, during the authentication process and, thus, prevents spoofing attacks at the data acquisition module [55]. Driver-Auth data acquisition module acquires data from three modalities, i.e., voice, swipe, and face, as described in Section 4.3.…”

Section: Liveness Detection and Preventing Spoofing Attacksmentioning

confidence: 99%

DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

Gupta

Buriro

Crispo

2019

Computers & Security

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Section: Liveness Detection and Preventing Spoofing Attacksmentioning

confidence: 99%

DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

Gupta

Buriro

Crispo

2019

Computers & Security

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“…The system of biometric authentication can be defined as a computer vision-based system that employsthe human body, for instance, Face, Fingerprint, Iris, DNA, Voice and/or behavioural characteristics like passwords, signatures, etc., in order to determine a particular personality to activate the authentication based on the results of the diagnosing process (Rute and Louro, 2014). Besides, past these recent years, with the technology advancement, digital biological data have emerged as a common application in various fields for assurance of critical security, for example, border control and airports banking processes.…”

Section: Introductionmentioning

confidence: 99%

Liveness Detection from Real user, Printed Pictures and Pictures on Mobile Devices from Low Resolution Webcam

Amer¹,

Rajamanickam²,

Abboud³

2017

Journal of Computer Science

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Biometrics data have emerged as one of the most widely used technologies for validation of identity in various sectors. Nevertheless, spoof biometric data are used by attackers to get access to their targets. Hence, a number of approaches have been initiated to detect these spoofed biometric data. As such, this article proposed a complete methodology for liveness detection using low camera resolution, primarily because vast studies do rely on image quality, eyelid motion and facial expression to investigate spoof images. Nevertheless, spoof attacks cannot be diagnosed from low quality images or recorded video on mobile devices. Therefore, this paper initiates a cutting-edge technique to identify spoof attack from printed pictures, as well as videos recorded on mobile devices and built-in low resolution webcam. Moreover, by detecting the movements at the eye region and weighing these movements from a number of opted frames from recorded video, the standard deviation of these weighted movements were determined and finally, the results of these standard deviation values were compared with the priory estimated threshold values retrieved from this study. Furthermore, due to the nature of the data employed in this study, the researchers generated some data for real users by using low resolution building webcam device by recording the face images of the users on mobile device. With that, 100 various videos were used to predict the threshold value for liveness detection. As a result, this method had been successful in analysing user liveness with an accuracy of 97.6%. On top of that, further experiment is required to look into this method with bigger data set.

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An Enhanced Generative Adversarial Network Model for Fingerprint Presentation Attack Detection

Anshul,

Jha,

Jain

et al. 2024

Lecture Notes in Computer Science

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Liveness Detection in Biometrics

Cited by 13 publications

References 2 publications

DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

Liveness Detection from Real user, Printed Pictures and Pictures on Mobile Devices from Low Resolution Webcam

An Enhanced Generative Adversarial Network Model for Fingerprint Presentation Attack Detection

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