Toward Open-Set Face Recognition

Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.Comment: Accepted for Publication in CVPR 2017 Biometrics Worksho

ILR Faculty Publications 2015-2016

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.FacultyPublications_2015_16.pdf: 21 downloads, before Oct. 1, 2020

Dynamic Template Adjustment in Continuous Keystroke Dynamics

Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.Keystroke dynamics is one of behavioural biometric characteristics which can be employed for continuous user authentication. As typing style on a keyboard changes in time, the template adapting is necessary. No study covered this topic yet, as far as the author knows. This master thesis tries to fill this gap. Several classification techniques were exercised with help of keystroke data from 22 volunteers in order to test if they can be improved to unsupervised online classifiers. A significant improvement in impostor recognition was noted at one-class statistical classifier based on normed Euclidean distance. The impostor could make 23.7 % actions less than in offline version on average but the improvement was obseved with all test sets. In contrary, the genuine user recognition varied from user to user but it still kept at acceptable values.

Sample size for comparing negative binomial rates in noninferiority and equivalence trials with unequal follow-up times

We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies. Methods are provided for back-calculating the dispersion parameter based on the published summary results

Network externalities in biometrical identification

Biometrical identification has not been adopted to be a key technology in computer security as was hoped for, or to the extent the sophistication of the technology would promise. One reason for this might be that the application of biometrical identification has not yet gained wide enough scale, which leads to missing positive network externalities.In this paper the concepts of biometrical identification and network externalities are discussed, and an analysis is performed on why missing positive network externalities are hampering the advance of biometrical identification