&lt;title&gt;Security and matching of partial fingerprint recognition systems&lt;/title&gt;

Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

doi:10.1117/12.542338

Cited by 17 publications

(16 citation statements)

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“…this hash function would be to find out the actual minutia point locations given these triangle centers. We compared performance with fingerprint matching algorithm developed in [16] and using same set of fingerprints with identically extracted minutiae points. Also, since in configurations 1 and 2 we simply get another set of minutia points, we used matching algorithm of [16] to perform matching.…”

Section: Methodsmentioning

confidence: 99%

“…After alignment, the number of matching minutiae points determine how good the match is. In our work we use ideas similar to [15] and [16] to combine results of localized matchings into the whole fingerprint recognition algorithm. Localized matching consists of matching minutia triplets using such features as angles and lengths between minutia points.…”

Section: Minutiae Based Matchingmentioning

confidence: 99%

“…As in base fingerprint matcher [16] the localized set is determined by a particular minutia and few of its neighbors. The hashes are calculated for each localized set.…”

Section: Global Fingerprint Matching Using Hash Functionsmentioning

confidence: 99%

See 2 more Smart Citations

Symmetric hash functions for secure fingerprint biometric systems

Tulyakov

Farooq

Mansukhani

et al. 2007

Pattern Recognition Letters

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102

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Securing biometrics databases from being compromised is one of the most important challenges that must be overcome in order to demonstrate the viability of biometrics based authentication. In this paper we present a novel method of hashing fingerprint minutia and performing fingerprint identification in the hash space. Our approach uses a family of symmetric hash functions and does not depend on the location of the (usually unstable) singular points (core and delta). In fact, most approaches of hashing minutia and developing a cancellable system described in the literature assume the location of the singular points. Others assume a pre-alignment between the test and the stored fingerprint templates. These assumptions are unrealistic given that fingerprints are very often only partially captured by the commercially available sensors. The Equal Error Rate (EER) achieved by our system is about 3%. We also present the performance analysis of a hybrid system that has an EER of about 2% which is very close to the performance of plain matching in the minutia space.

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Section: Methodsmentioning

confidence: 99%

Section: Minutiae Based Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Symmetric hash functions for secure fingerprint biometric systems

Tulyakov

Farooq

Mansukhani

et al. 2007

Pattern Recognition Letters

Self Cite

102

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show abstract

“…The baseline matching results and the reference alignment information were obtained using a minutia based fingerprint matching algorithm utilizing secondary features from the triplets of neighboring minutia. 19 This is a simulation that can be replaced by techniques outlined in section 3.3. However, as we are primarily concerned with the security of the vault within a reasonable matching scheme, this simulation fits our needs.…”

Section: Unlocking the Vaultmentioning

confidence: 99%

Security analysis for fingerprint fuzzy vaults

et al. 2013

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In this work we place some of the traditional biometrics work on fingerprint verification via the fuzzy vault scheme within a cryptographic framework. We show that the breaking of a fuzzy vault leads to decoding of Reed-Solomon codes from random errors, which has been proposed as a hard problem in the cryptography community. We provide a security parameter for the fuzzy vault in terms of the decoding problem, which gives context for the breaking of the fuzzy vault, whereas most of the existing literature measures the strength of the fuzzy vault in terms of its resistance to pre-defined attacks or by the entropy of the vault. We keep track of our security parameter, and provide it alongside ROC statistics. We also aim to be more aware of the nature of the fingerprints when placing them in the fuzzy vault, noting that the distribution of minutiae is far from uniformly random. The results we show provide additional support that the fuzzy vault can be a viable scheme for secure fingerprint verification.

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“…Image size is 374 × 388 and the resolution is 500dpi. To evaluate the methodology of adapting a gaussian kernel to the local ridge curvature of a fingerprint image, we modified Gabor-based fingerprint enhancement algorithm [15,16] with two kernel sizes: the smaller one in high-curvature regions and the larger one in pseudo-parallel ridge regions, minutiae are detected using chaincode-based contour tracing [17], the fingerprint matcher developed by Jea et al [18] is used for performance evaluation. Our methodology has been tested on low quality images from FVC2002.…”

Section: Resultsmentioning

confidence: 99%

Robust Point-Based Feature Fingerprint Segmentation Algorithm

Tulyakov

Govindaraju

Advances in Biometrics

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Abstract. A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.

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<title>Security and matching of partial fingerprint recognition systems</title>

Cited by 17 publications

References 0 publications

Symmetric hash functions for secure fingerprint biometric systems

Symmetric hash functions for secure fingerprint biometric systems

Security analysis for fingerprint fuzzy vaults

Robust Point-Based Feature Fingerprint Segmentation Algorithm

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