Grip-pattern recognition : applied to a smart gun

Shang, Xiaoxin

doi:10.3990/1.9789036527323

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…This type of biometric recognition has been investigated by the New Jersey Institute of Technology, 4,5 by Belgian weapon manufacturer FN Herstal, and by us. [6][7][8][9][10][11][12] The only results reported, besides ours, were published in Ref. 5, which is different from the one reported here in various aspects.…”

Section: Introductioncontrasting

confidence: 64%

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Grip-pattern verification for a smart gun

Shang

Veldhuis

2008

J. Electron. Imaging

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In the biometric verification system of a smart gun, the rightful user of the gun is recognized based on grip-pattern recognition. It was found that the verification performance of grip-pattern recognition degrades strongly when the data for training and testing the classifier, respectively, have been recorded in different sessions. The major factors that affect the verification performance of this system are the variations of pressure distribution and hand position between the probe image and the gallery image of a subject. In this work, three methods are proposed to reduce the effect of the variations by using different sessions for training, image registration, and classifier fusion. Based on these methods, the verification results are significantly improved.

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Section: Introductioncontrasting

confidence: 64%

“…Further research has shown that these variations are the main reason for the unsatisfactory across-session verification results. 11 Therefore, to improve the recognition results, the effect of the across-session variations of data needs to be reduced.…”

Section: Introductionmentioning

confidence: 99%

Grip-pattern verification for a smart gun

Shang

Veldhuis

2008

J. Electron. Imaging

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“…In the list above the first two items LOW FALSE REJECTION and LOW FALSE ACCEPTANCE depend on the performance of the biometric recognition algorithm which are the subject of the forthcoming thesis of Xiaoxing Shang [71]. The SECURE SEAL and ROBUST SENSOR can be considered as engineering challenges which can be addressed in a follow up project.…”

Section: Resultsmentioning

confidence: 99%

Cryptographic keys from noisy data : theory and applications

Buhan¹

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Biometric security systems that verify a person's identity by scanning fingers, hands, eye or face are becoming more and more common. As a result biometrics is one of the fastest growing industries. Applications for biometrics range from homeland security (for example the European biometric passport), physical access to various facilities (banks, amusement parks, office buildings, computer terminals, etc) and health and social services.Utilizing biometrics for personal authentication is more convenient and than current methods such as passwords or PINs (nothing to carry or remember). Another important advantage of biometric authentication is that it links events to a user (passwords or token can be lost or stolen) and is becoming socially acceptable and inexpensive. Biometric authentication requires comparing a registered or enrolled biometric sample (biometric template or identifier) against a newly captured biometric sample (for example, a fingerprint captured during a login).However, biometric authentication is not perfect and the output of a biometric authentication system can be subject to errors due to imperfections of the classification algorithm, poor quality of biometric samples, or an intruder who has tampered with the biometric authentication systems. Although biometric authentication is intended primarily to enhance security, storing biometric information in a database introduces new security and privacy risks, which increase if the database is connected to a network. This is the case in most practical situations.The most severe threats are: impersonation, where an attacker steals templates from a database and constructs a synthetic biometric sample that passes authentication; irrevocability, where once compromised, biometrics cannot be updated or reissued; privacy, which is the exposure of sensitive personal information without the consent of the owner. A solution to these threats is to apply templateprotection techniques, which make it hard for an attacker to recover the biometric data from the templates.This thesis looks at security aspects of biometric authentication and proposes solutions to mitigate the risk of an attacker who tries to misuse biometric information or who bypasses modules of biometric systems to achieve his malicious goals. vii Our contribution is threefold. Firstly we propose 3W-tree, an analysis tool used to identify critical attack scenarios for a biometric system. We apply the 3W-tree design tool to the SmartGun biometric recognition system with the purpose of identifying critical security issues. Secondly, we explore the challenges of secure template protection, which are both theoretical and practical and we put forward solutions to part of the issues. Thirdly, we present a practical solution to the secure template transfer, which should allow transfer of the biometric traits between two biometrically enabled devices when no security infrastructure is available and the users are no security experts. viii AcknowledgementsThe last four years were an unforgettable exp...

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“…Access to the vault of a bank will require a low FAR, the slightly higher FRR is an acceptable loss. Grip pattern recognition on a police firearm [61] will require a low FRR because the implications of a false reject are life threatening. In the right part of Figure 1.3 we show a Receiver Operating Characteristic (ROC).…”

Section: False Accept Rate and False Reject Ratementioning

confidence: 99%

Face recognition : a landmarks tale

Beumer¹

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Grip-pattern recognition : applied to a smart gun

Cited by 3 publications

References 39 publications

Grip-pattern verification for a smart gun

Grip-pattern verification for a smart gun

Cryptographic keys from noisy data : theory and applications

Face recognition : a landmarks tale

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