Fingerprint shell: Secure representation of fingerprint template

Moujahdi, Chouaib; Bebis, George; Ghouzali, Sanaa; Rziza, Mohammed

doi:10.1016/j.patrec.2014.04.001

Cited by 64 publications

(41 citation statements)

References 16 publications

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“…Table 4 and 5 depicts the performance comparision in terms of d-prime and K-S test values obtained respectively. Both these values are better compared to [1,12] in same key and different key scenarios.…”

Section: Comparision With Existing Methodsmentioning

confidence: 91%

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k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection

Sandhya

Prasad

2015

2015 International Conference on Biometrics (ICB)

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In this paper we focus on constructing k-Nearest Neighborhood Structure(k − NNS) for minutiae points in a fingerprint image. For each minutiae point in a fingerprint, a k − NNS is constructed taking the local and global features of minutiae points. This structure is quantized and mapped onto a 2D array to generate a fixed length 1D bit-string. Further this bit string is applied with a DF T to generate a complex vector. Finally the complex vector is multiplied by a user specific random matrix to generate the cancelable template. We tested our proposed method on database F V C2002 and experimental results depicts the validity of the proposed method in terms of requirements of cancelable biometrics namely diversity, accuracy, irreversibility and revocability.978-1-4799-7824-3/15/$31.00

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Section: Comparision With Existing Methodsmentioning

confidence: 91%

“…Minutiae based systems for fingerprint template protection can be classified into two types : Registration based methods [13,18,12] and Registration-free(alignment-free) methods [1,18,9,8]. In the former, the fingerprint image need to be pre-aligned according to the position of the singular points.…”

Section: Related Workmentioning

confidence: 99%

k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection

Sandhya

Prasad

2015

2015 International Conference on Biometrics (ICB)

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“…Researchers used different types of fingerprint features. Some approaches extracted not only minutiae but also singular point and other features [5,6,15,19]. The resulted template representation was also different.…”

Section: Biometric Template Protectionmentioning

confidence: 99%

Evaluating biometrics fingerprint template protection for an emergency situation

Mon

Vasupongayya

Karnjanadecha

et al. 2019

Teh. glas. (Online)

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Biometric template protection approaches have been developed to secure the biometric templates against image reconstruction on the stored templates. Two cancellable fingerprint template protection approaches namely minutiae-based bit-string cancellable fingerprint template and modified minutiae-based bit-string cancellable fingerprint template, are selected to be evaluated. Both approaches include the geometric information of the fingerprint into the extracted minutiae. Six modified fingerprint data sets are derived from the original fingerprint images in FVC2002DB1_B and FVC2002DB2_B by conducting the rotation and changing the quality of original fingerprint images according to the environment conditions during an emergency situation such as wet or dry fingers and disoriented angle of fingerprint images. The experimental results show that the modified minutiae-based bit-string cancellable fingerprint template performs well on all conditions during an emergency situation by achieving the matching accuracy between 83% and 100% on FVC2002DB1_B data set and between 99% and 100% on FVC2002DB2_B data set.

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“…The indexing features used were ridge count, triangle sign, reference point features of minutiae and its relative direction. Another indexing technique [19] involves construction of fingerprint shell which was a special triangle curve formed from the minutiae features. Hausdorff distance was used to match the query and database templates.…”

Section: Related Workmentioning

confidence: 99%

A novel fingerprint indexing scheme using dynamic clustering

Jain

Prasad

2016

J Reliable Intell Environ

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With the increase in adoption of biometric data for various identification processes, the need for efficient indexing techniques is also on the rise as it is computationally impossible to match the query template against every enrolled entry in the database. In this paper, we have proposed two robust feature extraction techniques from fingerprint images followed by an indexing method based on clustering and m-ary trees. We extract robust translation and rotation invariant features from O(n) triangles obtained from the triangle spiral constructed for every fingerprint. The other method involves construction of M rectangles around every reference minutiae and calculation of features based on neighboring relation. Experiments with the benchmark databases confirm the superiority of our approach to the other existing techniques.

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Fingerprint shell: Secure representation of fingerprint template

Cited by 64 publications

References 16 publications

k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection

k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection

Evaluating biometrics fingerprint template protection for an emergency situation

A novel fingerprint indexing scheme using dynamic clustering

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