Cognitive-Based Biometrics System for Static User Authentication

Hamdy, Omar; Traoré, Issa

doi:10.1109/icimp.2009.23

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…All identified systems utilize static challenges like clicking points on screen [36], navigating through a maze [37] or clicking special rhythms [38] to collect movement and keystroke information. Combination with additional features is done by Hamdy et al [39], who tried to measure short-term memory and perceptual capabilities. However, all mouse-based systems show high error rates.…”

Section: Authentication Systems Overview 311 Conventional Authenticmentioning

confidence: 99%

Authentication Systems for Securing Clinical Documentation Workflows

Schwartze

Haarbrandt²,

Fortmeier³

et al. 2014

Methods Inf Med

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Integration of electronic signatures and timestamps to secure clinical archiving and KeywordsSecurity, digital signature, electronic signature, authentication system Summary Context: Integration of electronic signatures embedded in health care processes in Germany challenges health care service and supply facilities. The suitability of the signature level of an eligible authentication procedure is confirmed for a large part of documents in clinical practice. However, the concrete design of such a procedure remains unclear. Objective: To create a summary of usable user authentication systems suitable for clinical workflows.

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Section: Authentication Systems Overview 311 Conventional Authenticmentioning

confidence: 99%

Authentication Systems for Securing Clinical Documentation Workflows

Schwartze

Haarbrandt²,

Fortmeier³

et al. 2014

Methods Inf Med

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show abstract

“…Besides this system, many other novel biometric systems expect to give an alternative to the well known biometric systems based on fingerprints. There are analyses of systems based on hand geometry [ 22 ], on finger geometry [ 23 ], on finger veins [ 24 ] or even on the mouse dynamics [ 25 ], amongst others.…”

Section: Test Acoustic Profiles For Biometric Applicationsmentioning

confidence: 99%

Performance Evaluation of a Biometric System Based on Acoustic Images

Fuente

Val

Jiménez

et al. 2011

Sensors

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An acoustic electronic scanning array for acquiring images from a person using a biometric application is developed. Based on pulse-echo techniques, multifrequency acoustic images are obtained for a set of positions of a person (front, front with arms outstretched, back and side). Two Uniform Linear Arrays (ULA) with 15 λ/2-equispaced sensors have been employed, using different spatial apertures in order to reduce sidelobe levels. Working frequencies have been designed on the basis of the main lobe width, the grating lobe levels and the frequency responses of people and sensors. For a case-study with 10 people, the acoustic profiles, formed by all images acquired, are evaluated and compared in a mean square error sense. Finally, system performance, using False Match Rate (FMR)/False Non-Match Rate (FNMR) parameters and the Receiver Operating Characteristic (ROC) curve, is evaluated. On the basis of the obtained results, this system could be used for biometric applications.

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“…In this first system, Mean Square Error (MSE) between two images of the same frequency and position is used to compare the acoustic profiles, defining a global error as the sum of the errors associated with each image of the profile. Using the Equal Error Rate (EER) as a quality indicator, this system obtained an EER value of 6.22%, such as other emerging biometric identification systems [ 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

Val

Fuente

Villacorta

et al. 2015

Sensors

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Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

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Cognitive-Based Biometrics System for Static User Authentication

Cited by 6 publications

References 12 publications

Authentication Systems for Securing Clinical Documentation Workflows

Authentication Systems for Securing Clinical Documentation Workflows

Performance Evaluation of a Biometric System Based on Acoustic Images

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

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