In this section, five image enhancement versions are described which apply curved regions for the ridge frequency estimation as introduced in Section 6.2 and the curved Gabor filter (CGF) introduced in Section 7.3. Acronyms are established, so that they can referenced in all subsequent sections. Due to time constraints and computational limitations, these enhancement variants are sample applications with an ad hoc parameter choice. Further parameter com-binations should be tested more systematically in order to investigate possible improvements by better parameter choices.
All enhancement versions have the same general layout: first, the OF is es-timated, either by a single method or by a combination of methods. Second, a raw, pixelwise RF image is obtained using the curved regions based RF es-timation and the OF from the first step. Afterwards, the RF is smoothed by averaging in a window. Finally, the curved Gabor filter is applied. Input ar-guments are the OF, the smoothed RF and the tuning parameters σx and σy. Segmentation or quality estimation is not explicitly performed in this experi-mentation. Since image enhancement is only performed for those pixels that are equipped with orientation and ridge frequency estimation, all other pixels are set to the global mean gray value and regarded as background.
Enhancement version E1. Two orientation fields are computed applying the line sensor method and the gradients based method with a smoothing window size of 33 pixels. The OFs are compared at each pixel. If the angle between both estimations is smaller than 15◦, the orientation of the combined OF is set to the average of the two. Otherwise, the pixel is marked as missing. Afterwards, all inner gaps are reconstructed as described in Section 4.4.2 and up to a radius of 16 pixels, the orientation of the outer proximity is extrapolated. The raw RF image is computed and smoothed in a window of size 49 pixels. Finally, the curved Gabor filter is applied withσx= 4 andσy= 4.
Enhancement version E2. Like in the previous version, two orientation fields are estimated using the line sensor method and the gradients based method (smoothing window size of 33 pixels). Here, the condition for averaging is an
angle smaller than 10◦ between both estimations and pixels for which both methods disagree (i.e. forming an angle ≥10◦) are not reconstructed. Since image enhancement is performed only for pixels endowed with an orientation estimation, this leads to gaps in the enhanced image which are filled with the mean gray value. The raw RF image is smoothed over windows of 33 pixels width and the CGF parameters areσx= 5 andσy= 4.
Enhancement version E3. The OF is estimated by the line sensor method.
A window size of 33 pixels is used for smoothing the raw RF image and the curved Gabor filter parameters are σx= 6 andσy= 7.
Enhancement version E4. Here, the line sensor method is applied with a grid size of 6 pixels (instead of 8 pixels which is used in all cases when the grid size is not explicitly stated). The averaging window for RF smoothing is 49 pixels wide andσx= 6, σy = 7 are the tuning parameters of the CGF.
Enhancement version E5. For version E5, the OF is estimated using the gra-dients based method with an averaging window of size 33 pixels. The ridge frequency is smoothed over windows of 65 pixels and the curved Gabor filter is applied with σx= 4 andσy= 4.
8.3.1 Results and Discussion
Table 8.2 specifies the EERs which are obtained, if the the three algorithms BZ3, VFM and VFG (see Section 2.4.2) are applied to the original images and enhanced images using version E1. On comparing the results, it is important to bear in mind that the free NIST software binarizes the grayscale input image and extracts the minutiae without performing any image enhancement, whereas the two algorithms derived from the Neurotechnology VeriFinger 5.0 SDK, VFM and VFG, have a built-in image enhancement which can not be turned off. In the case of these two algorithms, the test should be understood as a compar-ison of the VeriFinger built-in image enhancement versus a pre-enhancement using version E1 as described in Section 8.3 followed by the VeriFinger built-in enhancement. Bearing in mind that the VeriFinger software belonged to the top five algorithms in the FVC competitions, it is quite astonishing that EER reductions of up to 75 % are possible by the additional pre-enhancement using version E1. On the other hand, the results on FVC 2002 database 1 remind us of the risk that image enhancement can also impair the performance.
Basically, there are two types of errors which can occur during the minutiae extraction stage: first, true minutiae can be missed and second, false minutiae can be extracted. The image enhancement techniques we regard in this thesis aim at reducing the number of extraction errors. Ideally, fewer true minutiae are missed after the enhancement without introducing additional false minutiae.
The image enhancement version E1 is well suited to function as a stand-alone image enhancement step, whereas e.g. version E2 is specifically designed for en-hancing only those parts of the image which can be regard as reliable in terms of agreeing orientation estimations by the line sensor and gradients based method.
Matcher: BZ3 Original images Enhancement E1 EER Change
Matcher: VFM Original images Enhancement E1 EER Change
FVC2000 DB1 4.5 % 1.65 % - 63.3 %
Matcher: VFG Original images Enhancement E1 EER Change
FVC2000 DB1 4.37 % 1.03 % - 76.4 %
Table 8.2: EERs for the original and enhanced images. For each entry, a full FVC verification test with 7750 recognition attempts was conducted. BZ3, VFM and VFG denote the three algorithms described in Section 2.4.2.
E2 targets for improving the clarity of the ridge structure in these parts, so that further true minutiae can be discovered without adding false minutiae. On the downside, a substantial amount of information is lost, because the regions in which the OF estimations disagree, i.e. forming an angle ≥ 10◦, are ignored and set to the mean gray value. For these reasons, E2 is unsuitable as a stand-alone enhancement method. An obvious concept, however, is the combination of the minutiae from the original and enhanced image following the idea that additional true minutiae are discovered in the foreground of the enhanced image and that a combined template will perform better than each of the individual templates. This notion is examined in the next section.