A coherent framework for fingerprint analysis: are fingerprints Holograms?

Larkin, Kieran G.; Fletcher, Peter A.

doi:10.1364/oe.15.008667

Cited by 55 publications

(59 citation statements)

References 22 publications

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“…The AM-FM fingerprint model proposed by Larkin and Fletcher [13] represents a fingerprint image I as a hologram, i.e., consisting of 2D amplitude and frequency modulated fringe pattern:…”

Section: Am-fm Fingerprint Modelmentioning

confidence: 99%

“…However, this approach introduces many spurious minutiae around the intersections of regions generated from different minutiae; these spurious minutiae cannot be controlled. Feng and Jain [8] utilized the amplitude and frequency modulated (AM-FM) model [13] to reconstruct fingerprints, where the phase, consisting of continuous phase and spiral phase (corresponding to minutiae), completely determines the ridge structure and minutiae. Continuous phase reconstruction is therefore a critical step in the AM-FM model based fingerprint reconstruction.…”

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confidence: 99%

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Learning Fingerprint Reconstruction: From Minutiae to Image

Cao

Jain

2015

IEEE Trans.Inform.Forensic Secur.

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Abstract-The set of minutia points is considered to be the most distinctive feature for fingerprint representation and is widely used in fingerprint matching. It was believed that the minutiae set does not contain sufficient information to reconstruct the original fingerprint image from which minutiae were extracted. However, recent studies have shown that it is indeed possible to reconstruct fingerprint images from their minutiae representations. Reconstruction techniques demonstrate the need for securing fingerprint templates, improve the template interoperability and improve fingerprint synthesis. But, there is still a large gap between the matching performance obtained from original fingerprint images and their corresponding reconstructed fingerprint images. In this paper, the prior knowledge about fingerprint ridge structures is encoded in terms of orientation patch and continuous phase patch dictionaries to improve the fingerprint reconstruction. The orientation patch dictionary is used to reconstruct the orientation field from minutiae, while the continuous phase patch dictionary is used to reconstruct the ridge pattern. Experimental results on three public domain databases (FVC2002 DB1 A, FVC2002 DB2 A and NIST SD4) demonstrate that the proposed reconstruction algorithm outperforms the stateof-the-art reconstruction algorithms in terms of both i) spurious and missing minutiae and ii) matching performance with respect to type-I attack (matching the reconstructed fingerprint against the same impression from which minutiae set was extracted) and type-II attack (matching the reconstructed fingerprint against a different impression of the same finger).Index Terms-fingerprint reconstruction, orientation patch dictionary, continuous phase patch dictionary, minutiae set, AM-FM model.

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“…The AM-FM fingerprint model proposed by Larkin and Fletcher [13] represents a fingerprint image I as a hologram, i.e., consisting of 2D amplitude and frequency modulated fringe pattern:…”

Section: Am-fm Fingerprint Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Learning Fingerprint Reconstruction: From Minutiae to Image

Cao

Jain

2015

IEEE Trans.Inform.Forensic Secur.

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“…Examples of synthetic fingerprints generated by this method are shown in Figure 2 Viewing fingerprints as holograms (cf. [21]), a given global orientation field yields the continuous phase onto which so called spiral phases can be added giving minutiae at specific predefined locations while other minutiae occur due to continuity constraints. This allows for synthetic fingerprint generation and four example images generated by the software of Bicz [20] are displayed in Figure 2 (c) to (f).…”

Section: Construction and Reconstruction Of Fingerprintsmentioning

confidence: 99%

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

Gottschlich

Huckemann

2014

IET biom.

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In this study 1 we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a non-parametric distribution based method using second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This 'test of realness' can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.

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“…Larkin and Fletcher [11] proposed to represent a fingerprint image as a 2D amplitude and frequency modulated (AM-FM) signal:…”

Section: Fingerprint Representationmentioning

confidence: 99%

“…According to the demo software provided by the author, only a partial fingerprint (around the core) can be generated and the orientation field of each of the four fingerprint patterns is fixed. Larkin and Fletcher [11] proposed a fingerprint analysis framework based on AM-FM model where a fingerprint image is decomposed into the four components of the model. Separate compression of each component leads to a compressed fingerprint with an impressive compression factor of 239.…”

Section: Introductionmentioning

confidence: 99%

FM Model Based Fingerprint Reconstruction from Minutiae Template

Feng

Jain

2009

Advances in Biometrics

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Abstract. Minutiae-based representation is the most widely adopted fingerprint representation scheme. The compactness of minutiae template has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. However, these reconstruction techniques have a common weak point: many spurious minutiae, not included in the original minutiae template, are generated in the reconstructed image. Moreover, some of these techniques can only reconstruct a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed, which not only reconstructs the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. A fingerprint image is modeled as a 2D Frequency Modulation (FM) signal whose phase consists of the continuous part and the spiral part (which corresponds to minutiae). An algorithm is proposed to reconstruct the continuous phase from minutiae. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against the different impressions of the original fingerprint) using a commercial fingerprint recognition system. Both types of attacks were shown to be successful in deceiving the fingerprint system.

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A coherent framework for fingerprint analysis: are fingerprints Holograms?

Cited by 55 publications

References 22 publications

Learning Fingerprint Reconstruction: From Minutiae to Image

Learning Fingerprint Reconstruction: From Minutiae to Image

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints

FM Model Based Fingerprint Reconstruction from Minutiae Template

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