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Vascular Pattern Recognition: And its Application in Privacy-Preserving Biometric Online-Banking Systems

representation is often based on (ii), thus requires more processing but less storage. The number of minutiae points will vary depending on noise and other influencing factors, thus it can not be considered to be of fixed-length and structure. Efficient post-processing based on simple rules may remove false minutiae and improve recognition accuracy. For example the following situations can be easily prevented: clusters of minutiae points are likely occurring due to noise; endpoints in close proximity to branch points are likely to be caused by skeletonization errors; close endpoints facing each other are likely caused by segmentation errors and do not represent an actual break in the blood vessels. Perhaps the largest advantage is that minutiae-based systems are well researched for fingerprint systems and the already generated knowledge can be applied in the context of vein pattern recognition. Also template protection schemes like the fuzzy vault [108] were specifically designed for (fingerprint) minutiae systems and could be adapted to vein minutiae. In Chapter 9 we show – based on knowledge from fingerprint systems – how minutiae points are transformed into a fixed-length and structure representation that is alignment-free. 2.6.2 Comparison Strategies The HDS template protection scheme works on fixed-length vectors: simplified it can be stated that each element R(i) of the reference vector at position i is compared to the corresponding element P (i) of the probe vector. Approaches for feature comparison based on sophisticated strategies can not be applied here. A decent biometric performance based on a comparison of element pairs is therefore required to find approaches that could be utilized in conjunction with the HDS. Current survey articles about vein recognition can be found in [144, 265] but they are very limited. A new attempt to capture vein recognition related articles is given in Table 2.3, 2.4, 2.5 and 2.6. To first structure the data, we arrange it according to the submodality, then it is sorted according to the author(s). The overview of the state of the art is complemented with information about the invariance to transformations and about the reported performance. However, the performances can be considered to be of limited value, oftentimes EERs around 1% are measured with limited possibilities for comparisons as discussed in Section 2.2.5 due to differences in the databases and the evaluation protocols. Our own proposals introduced in Part II and articles published with our contribution are highlighted in bold letters to clarify the relation with the state of the art. As can be seen from the tables, most approaches are pixel-based (i) and thus are of fixed length. Some approaches are skeleton-based (ii) and a number are minutiae-based (iii). The latter case is commonly not of fixed length. Only a few approaches consider transformation invariance to some extend [8, 30, 77–79, 127, 178, 218, 228, 266]. In some of those cases a registration or alignment of the reference and the probe is necessary [8, 30, 79, 178, 218, 218, 228], which is not applicable in the HDS. Others are not of fixed length [127, 228] and are therefore not fulfilling the requirements of the HDS. Another approach not filtered yet suffers from a very low number of features: the feature vectors extracted in [266] have a length of 10 elements which is not sufficient for a strong brute-force resistance of the HDSsecured templates. Based on the analysis of existing approaches there is clearly a need of feature representation and comparison strategies compatible with the HDS. The spectral minutiae introduced for fingerprint [261, 262] and our proposal for adapting them to vein minutiae [77, 78] is a promising approach on how to combine vein data from touch-free sensors with the HDS.