Local Features for Enhancement and Minutiae Extraction in Fingerprints

Abstract

Abstract-Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacian-like image pyramid is used to decompose the original fingerprint into sub-bands corresponding to different spatial scales. In a further step, contextual smoothing is performed on these pyramid levels, where the corresponding filtering directions stem from the frequency-adapted structure tensor (linear symmetry features). For minutiae extraction, parabolic symmetry is added to the local fingerprint model which allows to accurately detect the position and direction of a minutia simultaneously. Our experiments support the view that using the suggested parabolic symmetry features, the extraction of which does not require explicit thinning or other morphological operations, constitute a robust alternative to conventional minutiae extraction. All necessary image processing is done in the spatial domain using 1-D filters only, avoiding block artifacts that reduce the biometric information. We present comparisons to other studies on enhancement in matching tasks employing the open source matcher from NIST, FIS2. Furthermore, we compare the proposed minutiae extraction method with the corresponding method from the NIST package, mindtct. A top five commercial matcher from FVC2006 is used in enhancement quantification as well. The matching error is lowered significantly when plugging in the suggested methods. The FVC2004 fingerprint database, notable for its exceptionally low-quality fingerprints, is used for all experiments.

Survey on Fingerprint Enhancement Techniques

“…To create images containing only band limited signals of the original image, one of the three original images is expanded, g [2][3][4] by factor f and subtract each of them from the next lower level, yielding l 1-3 . [5]. The entire process is tabulated as shown in Table 1.…”

Transform Domain Fingerprint Identification Based on DTCWT

“…The pixel wise classifier uses three pixel features such as Coherence, mean and variance. Hartwig Front haler and Klaus Kollreider [19] used a multi grid representation of a discrete differential scale space enhancement strategy of fingerprint recognition system. The fingerprint image is decomposed using Laplacian Pyramid as relevant information is concentrated within a few frequency bands.…”

Minutiae-based Fingerprint Extraction and Recognition

“…Accordingly, all the DCT coefficients in each non-overlapping image were divided into 12 areas, where one feature was extracted from each one of these areas, generating 12 features for each non-overlapping image. (Fronthaler et al, 2008) proposed the use of an image-scale pyramid and directional filtering in the spatial domain for fingerprint image enhancement to improve the matching performance as well as the computational efficiency. Image pyramids or multiresolution processing is especially known from image compression and medical image processing.…”

“…Image pyramids or multiresolution processing is especially known from image compression and medical image processing. (Fronthaler et al, 2008) expected that all the relevant information to be concentrated within a few frequency bands. Furthermore, they have proposed Gaussian directional filtering to enhance the ridge-valley pattern of a fingerprint image using computationally cheap 1-D filtering on higher pyramid levels (lower resolution) only.…”