Angular bandpass at and different orders <i>n</i> ∈ {3, 20, 100} and their responses (last row).

<p>Angular bandpass at and different orders <i>n</i> ∈ {3, 20, 100} and their responses (last row).</p

Butterworth bandpass filter at <i>ω</i>_<i>L</i> = 0.3, <i>ω</i>_<i>H</i> = 1 and different <i>γ</i>, angular bandpass filter with , and their responses.

<p>1^st row: 1D Butterworth, 2^nd row: 2D Butterworth, 3^rd row: , 4^th row: , 5^th row: , 6^th row: their responses.</p

Segmented fingerprint images and their features of different methods for FVC2004_DB2_IM_65_7.

<p>(a) ground truth; (b, g) FDB, (c, h) Gabor, (d, i) Harris, (e, j, k, l) Mean-Variance-Coherence, (f, m) STFT.</p

Matlab Implementation of the FDB Method for Fingerprint Segmentation

<p>Matlab implementation of the method described in:</p> <p>"Filter Design and Performance Evaluation for Fingerprint Image Segmentation"</p> <p>by Duy Hoang Thai, Stephan Huckermann, and Carsten Gottschlich</p> <p>A preprint is available at:</p> <p>http://arxiv.org/abs/1501.02113</p> <p>The Benchmark for Fingerprint Segmentation Performance Evaluation<br>(manually marked ground truth information for 10560 images)<br>is available at:</p> <p>http://dx.doi.org/10.6084/m9.figshare.1294209</p

Overview over the segmentation by the FDB method: In the analysis step, the original image (top row, left) is transformed into the Fourier domain (second column) and filtered by the first DHBB factor obtaining 16 directional subbands (third and fourth columns).

<p>Next soft-thresholding is applied to remove spurious patterns (second row, third and fourth columns). In the synthesis step, the feature image (second column) is reconstructed from these subbands using the second DHBB factor. Finally, the feature image is binarized and the ROI is obtained by morphological operations. The estimated ROI (third row, left) is compared to manually marked ground truth segmentation (third row, right) in order to evaluate the segmentation performance.</p

Segmented fingerprint images and their features of different methods for FVC2002_DB3_IM_15_1.

<p>(a) ground truth; (b, g) FDB, (c, h) Gabor, (d, i) Harris, (e, j, k, l) Mean-Variance-Coherence, (f, m) STFT.</p

Four typical thresholding functions (red: hard, black: soft, green: semi-soft, magenta: nonlinear) are compared (top left).

<p>The following six pairs show an image and the visualization of the corresponding 1D cross section along the red line. F.l.t.r and top to bottom: the original image <i>f</i>[<b><i>k</i></b>], the coefficient <i>c</i>_<i>l</i>[<b><i>k</i></b>] and the thresholded coefficients <i>d</i>_<i>l</i>[<b><i>k</i></b>] for the soft, hard, semisoft and nonlinear thresholding operators. Comparing the four cross sections in the bottom row, we observe that soft-thresholding achieves the sparsest solution.</p

Examples of incorrectly segmented fingerprint images due to: (a) a ghost fingerprint on the sensor surface, (b) dryness of the finger, (c) texture artifacts in the reconstructed image, (d) wetness of the finger.

<p>The first row shows the segmentation obtained by the FDB method, the second row displays the reconstructed image and the last row visualizes the manually marked ground truth segmentation.</p

The morphological element.

<p>The morphological element.</p

Comparison of five image reconstruction strategies and their effect on the resulting segmentation.

<p>1^st, 2^nd columns: segmented images (error in percent) and reconstructed images for a low-quality image and 3^rd, 4^th columns for a good quality image. 1^st row: the proposed operator. 2^nd, 3^rd rows: maximum operator without and with the shrinkage operator <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154160#pone.0154160.e056" target="_blank">Eq (7)</a>, respectively. 4^th, 5^th rows: summation operator without and with the shrinkage operator <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154160#pone.0154160.e056" target="_blank">Eq (7)</a>, respectively.</p