Improving fingerprint alteration detection

Gottschlich, Carsten; Mikaelyan, Anna; Olsen, Martin Aastrup; Bigün, Josef; Busch, Christoph

doi:10.1109/ispa.2015.7306037

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…A typical fingerprint recognition system (FRS) performs fingerprint segmentation [ 55 ], orientation field estimation, image enhancement, minutiae extraction and fingerprint matching [ 56 – 60 ]. Methods for fingerprint liveness detection and fingerprint alteration detection [ 61 ] can be considered as add-on modules to a FRS which aim to protect against these two types of presentation attacks [ 24 ]. The mean computational runtime for computing a CCP histogram with n = 9 pixels patch size and b = 8 comparisons is 0.7 seconds per image for the Biometrika database and 0.8 seconds per image for the Crossmatch and Italdata databases.…”

Section: Resultsmentioning

confidence: 99%

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

Gottschlich

2016

PLoS ONE

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We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification.

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Section: Resultsmentioning

confidence: 99%

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

Gottschlich

2016

PLoS ONE

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“…In Tables 1 and 2, we report key features for the four typical strategies. For the stopping criterion (C1), we have used κ( ⋅ ) < 0.95 with κ from (11). This can be viewed as an extrinsic…”

Section: Compression Resultsmentioning

confidence: 99%

“…3. Adjust the QD model to the initial OF by minimising a suitable objective function, given by (11), see (Fig. 3c).…”

Section: Manually Marking Of Ofsmentioning

confidence: 99%

“…Given the OF of an XQD model, i.e. A(x, y; …), and an initial OF (x j , y j , θ j ), we can measure the deviation of the XQD model to the initial OF by the following objective function, which, depends on all parameters of the XQD model: (see (11)) We note that, if steps 1 and 2 have been performed manually by an expert, the remaining steps can be implemented to run (semi-)automatically by utilising a steepest descent method applied to the objective function (11).…”

Section: Manually Marking Of Ofsmentioning

confidence: 99%

“…Altered fingerprints often have a disturbed OF. Therefore, it is not surprising that in recent comparisons of features for alteration detection [11], some of the most effective features are related to the OF. In a nutshell, differentials of orientation fields by tensors in scale (DOFTS) [12] and orientation field analysis (OFA) [13] are based on the difference between an estimated OF and a smoother version of it, coherence (COH) [3] relies on the coherence of gradients and singular point density analysis (SPDA) [14] makes use of the fact that alterations tend to introduce additional singularities into a fingerprint.…”

Section: Introductionmentioning

confidence: 99%

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Perfect fingerprint orientation fields by locally adaptive global models

2016

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Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this paper, a novel approach is presented to globally model an OF combined with locally adaptive methods. We show that this model adapts perfectly to the 'true OF' in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution we describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and we establish the main underlying claim of high fidelity low parameter OF compression.

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