http://dl.gi.de/handle/20.500.12116/34220 Thu, 23 Jul 2026 13:32:54 GMT 2026-07-23T13:32:54Z http://dl.gi.de:80/bitstream/id/527c31b6-e03d-4632-8d65-ff9db5eec988/ http://dl.gi.de/handle/20.500.12116/34220 http://dl.gi.de/handle/20.500.12116/34243 Perspective Multiplication for Multi-Perspective Enrolment in Finger Vein Recognition Prommegger, Bernhard; Uhl, Andreas Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. It has been shown that its recognition accuracy heavily depends on a good alignment of the acquired samples. There are several approaches that try to reduce the impact of finger misplacement. However, none of this approaches is able to prevent all possible types of finger misplacements. As finger vein scanners are evolving towards contact-less acquisition, alignment problems, especially due to longitudinal finger rotation, are becoming even more important. One way to tackle this problem is capturing the vein structure from different perspectives during enrolment, but cost and complexity of capturing devices increases with the number of involved cameras. In this article, a new method to reduce the number of cameras needed for multi-perspective enrolment is presented. The reduction is achieved by introducing additional pseudo perspectives in-between two adjacent cameras. The obtained perspectives are used for additional comparisons during authentication. This way, the complexity of the enrolment devices can be reduced while keeping the recognition performance at a high level. Tue, 01 Jan 2019 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/34243 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34242 Development of 2,400ppi Fingerprint Sensor for Capturing Neonate Fingerprint within 24 Hours after Birth Koda, Yoshinori; Takahashi, Ai; Ito, Koichi; Aoki, Takafumi; Kaneko, Satoshi; Nzou, Samson Muuo Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas United Nations adopted the resolution “Sustainable Development Goals (SDGs),” which aims at solving the eradicating the poverty in all its forms and dimensions. One of the action plans is listed at “Goal 16 Target 16.9,” which clearly directs “By 2030, provide legal identity for all, including birth registration.” A fingerprint identification technology is one of the best solutions from the viewpoint of making a reliable identification system for the birth registration. However, collecting the fingerprint data from neonates is currently considered as one of the most difficult technology areas. Addressing this problem, we develop a novel high-resolution fingerprint sensor, whose image resolution is 2,400ppi. We collect fingerprint images from neonates within 24 hours after birth through the field research in Kenya. The experiments using our dataset demonstrates the effectiveness of our fingerprint sensor in neonate identification compared with 500ppi and 1,270ppi fingerprint sensors. Tue, 01 Jan 2019 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/34242 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34241 Fingerprint Pre-Alignment based on Deep Learning Dieckmann, Benjamin; Merkle, Johannes; Rathgeb, Christian Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Robust fingerprint pre-alignment is vital for identification systems and biometric cryptosystems based on fingerprint minutiae, where computation of a relative alignment by comparison of the fingerprints is inefficient or intractable, respectively. The pre-alignment is achieved through an absolute alignment, i. e. an alignment computed for each fingerprint independently, which can be applied for fingerprint registration to compensate for variations in the placement (translation) and rotation of the fingerprints prior to their comparison. In this work, a deep learning approach for absolute pre-alignment of fingerprints is presented. The proposed algorithm employs a siamese network (with CNNs as subnetworks) which is trained on synthetically generated fingerprints using horizontal/vertical translation and rotation as three regression coefficients. Evaluations are conducted on the FVC2000 DB2a and the MCYT fingerprint database. Compared to other published fingerprint pre-alignment methods, the presented scheme achieves higher accuracy w. r. t. rotation estimation and overall robustness. In addition, the proposed pre-alignment is applied as a pre-processing step in a Fuzzy Vault scheme. Tue, 01 Jan 2019 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/34241 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34239 Deep Domain Adaption for Convolutional Neural Network (CNN) based Iris Segmentation: Solutions and Pitfalls Jalilian, Ehsaneddin; Uhl, Andreas Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Addressing the lack of massive amounts of labeled training data, deep domain adaptation has been applied successfully in many applications of machine learning. We investigate the application of deep domain adaptation for CNN based iris segmentation, exploring available solutions and their corresponding strengths and pitfalls, with several major contributions. First, we provide a comprehensive survey of current deep domain adaptation methods according to the properties of data that cause the domains divergence. Second, after selecting credible methods, we evaluate their expedience in terms of iris segmentation performance. Third, we analyze and compare the performance against the state-of-the-art methods under these categories. Forth, potential shortfalls of current methods and several future directions are pointed out and discussed. Tue, 01 Jan 2019 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/34239 2019-01-01T00:00:00Z