This survey is intended for researchers and practitioners in the field of human analysis.
Privacy-enhancing technologies are technologies that implement fundamental data protection principles.
This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems.
Moreover, we demonstrate the feasibility of the generation by using a deep learning-based model for removing tattoos from face images.
To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision.
In recent years, increasing deployment of face recognition technology in security-critical settings, such as border control or law enforcement, has led to considerable interest in the vulnerability of face recognition systems to attacks utilising legitimate documents, which are issued on the basis of digitally manipulated face images.
Doppelg\"angers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials.
Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.
We present the first method for synthetic generation of contactless fingerprint images, referred to as SynCoLFinGer.
Due to their convenience and high accuracy, face recognition systems are widely employed in governmental and personal security applications to automatically recognise individuals.
Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption.
Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision.
This work summarises opinions of experts and findings of said events on the topic of demographic fairness in biometric systems including several important aspects such as the developments of evaluation metrics and standards as well as related issues, e. g. the need for transparency and explainability in biometric systems or legal and ethical issues.
In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems.
Results obtained from challenging cross-database experiments in which the analyzed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilizing deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.
The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries.
We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system.
no code implementations • 18 Feb 2021 • Marta Gomez-Barrero, Pawel Drozdowski, Christian Rathgeb, Jose Patino, Massimmiliano Todisco, Andras Nautsch, Naser Damer, Jannis Priesnitz, Nicholas Evans, Christoph Busch
Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life.
As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted.
The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors.
All tested enhancer types exclusively use depth data as input, which differs from methods that enhance depth based on additional input data such as visible light color images.
no code implementations • 11 Jun 2020 • Kiran Raja, Matteo Ferrara, Annalisa Franco, Luuk Spreeuwers, Illias Batskos, Florens de Wit Marta Gomez-Barrero, Ulrich Scherhag, Daniel Fischer, Sushma Venkatesh, Jag Mohan Singh, Guoqiang Li, Loïc Bergeron, Sergey Isadskiy, Raghavendra Ramachandra, Christian Rathgeb, Dinusha Frings, Uwe Seidel, Fons Knopjes, Raymond Veldhuis, Davide Maltoni, Christoph Busch
Further, we present a new online evaluation platform to test algorithms on sequestered data.
Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition.
In addition, the application of deep face representations for differential morphing attack detection algorithms is investigated.
Cryptography and Security
In this paper, we provide a comprehensive analysis of periocular-based sex-prediction (commonly referred to as gender classification) using state-of-the-art machine learning techniques.