Fig 1.
Overview over the proposed feature vector computation.
Every foreground pixel of a fingerprint (A) is considered as the center of a small image patch. Instead of using the original patches (B) and their DCT coefficients (C), we take the local orientation into account to obtain rotation invariant patches (D) and their DCT coefficients (E). We compute the binary pattern (F) by comparing selected pairs of two DCT coefficients from (E), see Eq (2). The pattern (F) is converted intoa bin number (G), see Eq (3). A histogram (H) summarizes the relative frequency of occurrence of all local patterns for an image. (For illustrative purposes only, patch sizes are here 17×17 pixels, and coefficients with index = 1 are set to zero in (C) and (E), and (F-H) show example descriptors.)
Fig 2.
Visualization of DCT coefficients for n = 9 (left) and corresponding index numbers (right).
Negative values are depicted in black or dark gray and positive coefficients are shown in white or light gray.
Fig 3.
The preprocessing steps for an input fingerprint image (a) are segmentation (b) and orientation field (OF) estimation (c,d).
Foreground pixels in (b) are shown in black, background pixels in white. The OF is visualized in (c) by red lines for every 16th pixel. In (d), orientations in degrees are encoded by gray values between 0 and 179, where 0 corresponds to x-axis and angles increase clock-wise.
Table 1.
Comparison of liveness detection methods in terms of accuracy in percent for LivDet 2013 databases [42].
Further results can be found in Table 7 of [42]. The description of CCP n×b bit can be found in Section 3.1.
Fig 4.
Example images from LivDet 2013 [42] after background removal by the FDB method [38].
The first row depicts images acquired on a Biometrika sensor, the second row from a Crossmatch sensor and the third row from an Italdata sensor. The leftmost three columns show images of real, alive fingers and rightmost three columns images of fake fingers (spoof material indicated in legend).