http://dl.gi.de/handle/20.500.12116/20083 2026-07-25T06:04:55Z http://dl.gi.de/handle/20.500.12116/4667 Recognizing infants and toddlers over an on-production fingerprint database Camacho,Vanina; Garella,Guillermo; Franzoni,Francesco; Di Martino,Luis; Carbajal,Guillermo; Preciozzi,Javier; Fernández,Alicia Brömme,Arslan; Busch,Christoph; Dantcheva,Antitza; Rathgeb,Christian; Uhl,Andreas It is widely known that biometric systems based on adults fingerprints have reached an outstanding performance when compared against other biometric traits. This explains their extensive use by governmental agencies in charge of citizen identification. Nevertheless, the performance is highly degraded when fingerprints of newborns or toddlers are used. In this work, we analyze the performance of existing solutions (both at sensor and matching level) using 45000 infants fingerprints taken from an on-production civilian database. We also propose a solution by zooming the input fingerprints with an interpolation factor based on ridges distances. The developed solution shows improvements in both fingerprint quality (NFIQ 2.0) as well as recognition performance. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/4666 Fingerprint Template Ageing vs. Template Changes Revisited Kirchgasser,Simon; Uhl,Andreas Brömme,Arslan; Busch,Christoph; Dantcheva,Antitza; Rathgeb,Christian; Uhl,Andreas This study investigates the impact of “ghost” fingerprint and minutiae information in 4 year time-span separated fingerprint datasets. A high amount of ghost fingerprints within the data, eventually a source for differences in acquisition conditions, might be responsible for recently reported template ageing effects. According to that, various experiments have been performed to get rid of this problematic image content and to compare the corresponding matching results to the performance figures using the non altered imprints. The analysis with respect to detected increased error rates exhibits very similar effects for all considered methods no matter if ghost fingerprint information is removed or not. Thus, ghost fingerprints are not responsible for the observed effects. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/4665 xTARP: Improving the Tented Arch Reference Point Detection Algorithm Merkle,Johannes; Tams,Benjamin; Dieckmann,Benjamin; Korte,Ulrike Brömme,Arslan; Busch,Christoph; Dantcheva,Antitza; Rathgeb,Christian; Uhl,Andreas In 2013, Tams et al. proposed a method to determine directed reference points in fingerprints based on a mathematical model of typical orientation fields of tented arch type fingerprints. Although this Tented Arch Reference Point (TARP) method has been used successfully for prealignment in biometric cryptosystems, its accuracy does not yet ensure satisfactory error rates for single finger systems. In this paper, we improve the TARP algorithm by deploying an improved orientation field computation and by integrating an additional mathematical model for arch type fingerprints. The resulting Extended Tented Arch Reference Point (xTARP) method combines the arch model with the tented arch model and achieves a significantly better accuracy than the original TARP algorithm. When deploying the xTARP method in the Fuzzy Vault construction of Butt et al., the false non-match rate (FNMR) at a security level of 20 bits is reduced from 7:4% to 1:7%. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/4663 Domain Adaptation for CNN Based Iris Segmentation Jalilian,Ehsaneddin; Uhl,Andreas; Kwitt,Roland Brömme,Arslan; Busch,Christoph; Dantcheva,Antitza; Rathgeb,Christian; Uhl,Andreas Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training model for CNN based iris segmentation. Based on our experiments, the proposed methods can effectively transfer the domains of source databases to those of the targets, producing new adapted databases. The adapted databases then are used to train CNNs for segmentation of iris texture in the target databases, eliminating the need for the target labeled data. We also indicate that training a specific CNN for a new iris segmentation task, maintaining optimal segmentation scores, is possible using a very low number of training samples. 2017-01-01T00:00:00Z