An effective biometric cryptosystem combining fingerprints with error correction codes

Li, Peng; Yang, Xin; Qiao, Hua; Cao, Kai; Liu, Eryun; Tian, Jie

doi:10.1016/j.eswa.2011.12.048

Cited by 73 publications

(28 citation statements)

References 28 publications

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“…Fuzzy Commitment was initially described in (Juels and Wattenberg, 1999) and is implemented in (Nagar et al, 2010), (Li et al, 2012), (Kaizhi and Aiqun, 2013). Two systems that use Fuzzy Commitment are described below.…”

Section: Fuzzy Commitmentmentioning

confidence: 99%

“…1.6 to 1), and rounding (ex 1.6 to 2). (Jin et al, 2012), (Kaizhi and Aiqun, 2013), (Li et al, 2012), (Mirmohamadsadeghi and Drygajlo, 2013), (Teoh and Kim, 2007), (Wang et al, 2010), , (Yang et al, 2014) quantize bitstrings derived from representations. (Arakala et al, 2007), , (Hartloff et al, 2013), (Jin et al, 2009), (Jin et al, 2010), (Junior et al, 2010), (Moon et al, 2007), (Nagar et al, 2010), (Nandakumar et al, 2007), quantize features.…”

Section: Quantizationmentioning

confidence: 99%

“…Error correction codes can be made with a variety of message length and error correction capacities. (Arakala et al, 2007), (Fan and Lin, 2009), (Jin et al, 2012), (Kaizhi and Aiqun, 2013), (Li et al, 2012), (Nagar et al, 2010), (Teoh and Kim, 2007), , (Yang et al, 2014) use error correction codes on the biometric. (Bringer et al, 2012), (Johnson et al, 2013b), (Nandakumar et al, 2007), (Scheirer and Boult, 2009) use error correction codes to correct a message as part the authentication process.…”

Section: Error Correcting Codesmentioning

confidence: 99%

“…For minutia pairs, this can be the distance between the points ( Boult et al, 2007), (Das et al, 2012), (Jin et al, 2010) use a pair representation. (Chen et al, 2006), (Jin et al, 2013), (Jin et al, 2009), (Li et al, 2012), (Parziale and Niel, 2004), , (Zheng et al, 2009) use generic triangles. (Chau and Soto, 2011), (Junior et al, 2010), , use Delaunay triangles, which restrict the triangles to a limited neighborhood.…”

Section: Invariant Representationmentioning

confidence: 99%

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Remote authentication using vaulted fingerprint verification

Alzahrani

Boult

2014

SPIE Proceedings

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Fingerprint biometrics are increasingly used for identity verification. However, these require a careful balance of accuracy and privacy that is missing in many implementations.This dissertation describes a new biometric matching system, the protection of an existing datatype, and a model for matching security with error correction codes.My research develops the Vaulted Verification (VV) system into Vaulted Fingerprint Verification (VFV) by implementing VV on a fingerprint minutia triangle representation.VFV is fully compatible with protected biometric datatypes. This is demonstrated by including PMCC, as well as a protected modification of PMCC. VFV allows for key exchange using the protected biometric template.Due to the importance of error correcting codes (ECC) in VFV, a model of security with ECC is developed. It is used to identify non-trivial potential attacker uses of ECC bits.To my parents and my family.

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Section: Fuzzy Commitmentmentioning

confidence: 99%

Section: Quantizationmentioning

confidence: 99%

Section: Error Correcting Codesmentioning

confidence: 99%

Section: Invariant Representationmentioning

confidence: 99%

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Remote authentication using vaulted fingerprint verification

Alzahrani

Boult

2014

SPIE Proceedings

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“…Systems that employ biometric traits to secure cryptographic keys are often called biometric cryptosystems [13]. The Fuzzy Commitment Scheme (FCS) [8] is one of the most popular biometric cryptosystems that has been applied successfully to iris [2,7,9,12,15], fingerprint [10], and face [1] biometrics. FCS binds cryptographic keys, encoded using Error Correction Codes (ECC), to binary biometric feature vectors using simple XOR operation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Commitment Scheme for Masked Iris Codes

Ouda¹

2013

IJCA

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The fuzzy commitment scheme is one of most popular biometric cryptosystems that aim at securing cryptographic keys using biometrics. Because of the high recognition accuracy exhibited by the iris, iris-based fuzzy commitment schemes, among other modalities, provide the most practical performance rates. Unfortunately, existing iris-based fuzzy commitment schemes do not incorporate noise masks, generated along with iris-codes to highlight unwanted regions of the iris, because there is no way to know the mask of the decoding iris sample in advance. Therefore, the decoding accuracy of iris-based fuzzy commitment schemes is much less than the recognition accuracy of the underlying iris recognition system. This paper presents an iris-based fuzzy commitment scheme that uses the noise mask of the encoding iris sample at both encoding and decoding stages. Experimental results show that the proposed scheme provides a remarkable improvement in the decoding accuracy of iris-based fuzzy commitment schemes.

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