Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Abstract

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

Improving fingerprint alteration detection

“…In this work we have used the same limited dataset from [8], however in order to avoid any impact of background area on the performance, all images were pre-segmented by the FDB method [9]. In total 116 altered fingerprint images and 180 unaltered, normal fingerprint images were used.…”

“…All fingerprint images have been preprocessed using the factorized directional bandpass (FDB) method [9]. First, the region of interest (ROI) has been estimated by the FDB method and next, images have been automatically adjusted by removing all rows and all columns which contain only background pixels.…”

Directional global three-part image decomposition

“…For the application of DG3PD to fingerprints, we are especially interested in the texture image v as a feature for subsequent processing steps like segmentation, orientation field estimation [32] and ridge frequency estimation [15], and fingerprint image enhancement [15,21]. The first of these processing steps is to separate the foreground from the background [25,57]. The foreground area (or region of interest) contains the relevant information for a fingerprint comparison.…”

“…In Fig. 10, we show further examples of segmentation results obtained using the texture image extracted by the DG3PD method and morphological postprocessing as described in [25,57].…”

“…DG3PD decomposition of a obtains a smooth cartoon image u (d) and a smooth and sparse texture image v (e). All positive coefficients of v are visualized as white pixels in f. The region of interest shown in g is estimated from v bin using morphological operations [57]. Image i is composed from b and h and decomposed by DG3PD into cartoon (j), texture (k), and residual (l) removal of complex background and the isolation of characters.…”