An analysis of BioHashing and its variants

Kong, Adams Wai-Kin; Cheung, King Hong; Zhang, David; Kamel, Mohamed S.; You, Jane

doi:10.1016/j.patcog.2005.10.025

Cited by 218 publications

(97 citation statements)

References 18 publications

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“…The authors report almost perfect performance rates for the generation of biometric hashes. In order to expose the true performance of BioHashing, Kong et al [9] presented an implementation of FaceHashing. It was found that the reported performance was achieved under the hidden assumption that random numbers are never lost, stolen, or compromised.…”

Section: Biometric T-fa Systemsmentioning

confidence: 99%

Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems

Rathgeb¹,

Uhl²

2010

Lecture Notes in Computer Science

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Abstract. Two-factor authentication has been introduced in order to enhance security in authentication systems. Different factors have been introduced, which are combined for means of controlling access. The increasing demand for high security applications has led to a growing interest in biometrics. As a result several two-factor authentication systems are designed to include biometric authentication.In this work the risk of result distortion during performance evaluations of two-factor authentication systems including biometrics is pointed out. Existing approaches to two-factor biometric authentication systems are analyzed. Based on iris biometrics a case study is presented, which demonstrates the trap of untruly increasing recognition rates by introducing a second authentication factor to a biometric authentication system. Consequently, several requirements for performance evaluations of two-factor biometric authentication systems are stated.

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Section: Biometric T-fa Systemsmentioning

confidence: 99%

Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems

Rathgeb¹,

Uhl²

2010

Lecture Notes in Computer Science

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“…The limitations of correction capacity affect the matching accuracy of the system. Many hashing based systems have also been developed but they too fail to achieve high accuracy in realistic settings [16].…”

Section: Introductionmentioning

confidence: 99%

Secure hamming distance based biometric authentication

Kulkarni

Namboodiri

2013

2013 International Conference on Biometrics (ICB)

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Concerns of privacy, template security and efficiency of biometric authentication systems have received considerable research attention in the recent past. Binary template representations have been shown to provide significant improvements in efficiency without compromising accuracy for a variety of biometric modalities including fingerprints, palmprints and iris. Most of the secure authentication protocols work with generic feature representations or propose new secure templates for specific modalities. In this work, we propose an authentication protocol that works with any binary biometric representation that employs hamming distance for comparison and convert it into a secure, privacy preserving protocol that provides template protection. We also provide the ability to use masks while computing the hamming distance. The protocol retains the accuracy of the underlying representation as demonstrated by our experiments.

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“…In most of these applications, biometric templates are stored in databases and/or smart cards, thus raising questions such as data security and privacy. Various biometric hashing methods, which mostly depend on random projections, are proposed to protect the biometrics data [1]- [5] in the literature. See Fig.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Projection Selection Based Face Image Hashing

Karabat

Erdoğan

2012

IEICE Trans. Inf. & Syst.

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SUMMARYFace image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods. key words: face image hashing, biometric security, privacy

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An analysis of BioHashing and its variants

Cited by 218 publications

References 18 publications

Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems

Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems

Secure hamming distance based biometric authentication

Discriminative Projection Selection Based Face Image Hashing

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