The goal of the project "Facial Metrics for EES" is to develop, implement and publish an open source algorithm for the quality assessment of facial images (OFIQ) for face recognition, in particular for border control scenarios. 1 In order to stimulate the harmonization of the requirements and practices applied for QA for facial images, the insights gained and algorithms developed in the project will be contributed to the current (2022) revision of the ISO/IEC 29794-5 standard.
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.
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.
Algorithmic decision systems have frequently been labelled as "biased", "racist", "sexist", or "unfair" by numerous media outlets, organisations, and researchers.
In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems.
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.
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.
Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition.