C
Christoph Busch
Researcher at Darmstadt University of Applied Sciences
Publications - 651
Citations - 12841
Christoph Busch is an academic researcher from Darmstadt University of Applied Sciences. The author has contributed to research in topics: Biometrics & Facial recognition system. The author has an hindex of 46, co-authored 585 publications receiving 9624 citations. Previous affiliations of Christoph Busch include Escuela Superior Politecnica del Litoral & University of Osnabrück.
Papers
More filters
Journal ArticleDOI
Deep Face Representations for Differential Morphing Attack Detection
TL;DR: Subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, and it is shown that algorithms based on deep face representations can achieve very high detection performance and robustness with respect to various post-processings.
Proceedings ArticleDOI
Multi-modal authentication system for smartphones using face, iris and periocular
TL;DR: A multi-modal biometric system, which uses face, periocular and iris biometric characteristics for authentication, which is tested on two different devices - Samsung Galaxy S5 smartphone and Samsung Galaxy Note 10.1 tablet.
Journal ArticleDOI
Cancelable multi-biometrics: Mixing iris-codes based on adaptive bloom filters
TL;DR: Adaptive Bloom filter-based transforms are applied in order to mix binary iris biometric templates at feature level, where iris-codes are obtained from both eyes of a single subject, achieving a compression of mixed templates down to 10% of original size.
Book ChapterDOI
Fingerprint Recognition with Embedded Cameras on Mobile Phones
TL;DR: A first step towards a novel biometric authentication approach applying cell phone cameras capturing fingerprint images as biometric traits is proposed and shows a biometric performance with an Equal Error Rate of 4.5% by applying a commercial extractor/comparator and without any preproccesing on the images.
Journal ArticleDOI
Detection of Face Morphing Attacks Based on PRNU Analysis
TL;DR: In scenarios where image sources and morphing techniques are unknown, the proposed PRNU-based morphing attack detector is shown to robustly distinguish bona fide and morphed face images achieving an average D-EER of 11.2% in the best configuration.