A New Biometric Template Protection using Random Orthonormal Projection and Fuzzy Commitment

Biometric template protection is one of most essential parts in putting a biometric-based authentication system into practice. There have been many researches proposing different solutions to secure biometric templates of users. They can be categorized into two approaches: feature transformation and biometric cryptosystem. However, no one single template protection approach can satisfy all the requirements of a secure biometric-based authentication system. In this work, we will propose a novel hybrid biometric template protection which takes benefits of both approaches while preventing their limitations. The experiments demonstrate that the performance of the system can be maintained with the support of a new random orthonormal project technique, which reduces the computational complexity while preserving the accuracy. Meanwhile, the security of biometric templates is guaranteed by employing fuzzy commitment protocol.Comment: 11 pages, 6 figures, accepted for IMCOM 201

Context-based texture analysis for secure revocable iris-biometric key generation

In this work we present an iris-biometric cryptosystem. Based on the idea of exploiting the most reliable components of iriscodes, cryptographic keys are extracted, long enough to be applied in common cryptosystems. The main benefit of our system is that cryptographic keys are directly derived from biometric data, thus, neither plain biometric data nor encrypted biometric data has to be stored in templates. Yet, we provide fully revocable cryptographic keys. Experimental results emphasize the worthiness of our approach

Устройство для перемещения датчиков в магнитном поле малогабаритного бетатрона

Рассматривается возможность увеличения точности измерений характеристик магнитного поля посредством более точной установки датчиков в исследуемой точке

Prolactin

During an oral glucose tolerance test (OGTT) glucose and insulin levels were measured in 26 patients with prolactin-producing pituitary tumours without growth hormone excess. Basal glucose and insulin levels did not differ from the values of an age-matched control group. After glucose load the hyperprolactinaemic patients showed a decrease in glucose tolerance and a hyperinsulinaemia. Bromocriptine (CB 154), which suppressed PRL, improved glucose tolerance and decreased insulin towards normal in a second OGTT. — Human PRL or CB 154 had no significant influence on insulin release due to glucose in the perfused rat pancreas. — These findings suggest a diabetogenic effect of PRL. CB 154 might be a useful drug in improving glucose utilization in hormone-active pituitary tumours

Research in graphic design

An invited and reviewed opinion piece about the current state of research in graphic/communication design

On the use of fingernail images as transient biometric identifiers

The significant advantages that biometric recognition technologies offer are in danger of being left aside in everyday life due to concerns over the misuse of such data. The biometric data employed so far focuses on the permanence of the characteristics involved. A concept known as ‘the right to be forgotten’ is gaining momentum in international law and this should further hamper the adoption of permanent biometric recognition technologies. However, a multitude of common applications are short-term and, therefore, non-permanent biometric characteristics would suffice for them. In this paper we discuss ‘transient biometrics,’ i.e. recognition via biometric characteristics that will change in the short term and show that images of the fingernail plate can be used as a transient biometric with a useful life-span of less than 6 months. A direct approach is proposed that requires no training and a relevant evaluation dataset is made publicly available

The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration.Peer reviewe