Aabrandt, AndreasOlsen, Martin AastrupBusch, ChristophBrömme, ArslanBusch, Christoph2018-11-192018-11-192012978-3-88579-290-1https://dl.gi.de/handle/20.500.12116/18300This paper discusses the use of betti numbers to characterize fingerprint and iris images. The goal is to automatically separate fingerprint images from nonfingerprint images; where non-fingerprint images of special interest are biometric samples which are not fingerprints. In this regard, an image is viewed as a triangulated point cloud and the topology associated with this construct is summarized using its first betti number - a number that indicates the number of distinct cycles in the triangulation associated to the particular image. This number is then compared against the first betti numbers of “n” prototype images in order to perform classification (“fingerprint” vs “non-fingerprint”). The proposed method is compared against SIVV (a tool provided by NIST). Experimental results on fingerprint and iris databases demonstrate the potential of the scheme.enText/Conference Paper1617-5468