CHARACTERIZING HABITUATION USING THE TIME-ON-TASK METRIC IN AN IRIS RECOGNITION SYSTEM

This thesis presents a characterization of biometric habituation in an iris recognition study using qualitative analysis of a distributed habituation survey and quantitative analysis of iris images collected in 2010 and 2012. The performed analyses answered the following two questions: a) How consistently does the biometric community define habituation?; and b) Does the time-on-task variable provide enough evidence to indicate the existence of habituation in an iris recognition system? The qualitative analysis examined responses to 12 habituation-related questions from 13 biometric experts to identify common themes that not only determined definition consistency but also characterized critical components often omitted from habituation definitions. Upon completion of the survey analysis, this study concluded that while aspects of habituation were universally understood, habituation in its entirety was not. The quantitative analysis examined trends in mean time-on-task using number of visits as a covariate. Subjects repeatedly (20 captures per visit and 25 maximum attempts per visit) interacted with an iris recognition camera, returning for at least eight visits. The trends in the resulting time-on-task, image quality and matching performance indicated that habituation effects were identifiable near the end of the 2012 collection

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

An assessment of the usability of biometric signature systems using the human-biometric sensor interaction model’

Signature biometrics is a widely used form of user authentication. As a behavioural biometric, samples have inherent inconsistencies which must be accounted for within an automated system. Performance deterioration of a tuned biometric software system may be caused by an interaction error with a biometric capture device, however, using conventional error metrics, system and user interaction errors are combined, thereby masking the contribution by each element. In this paper we explore the application of the Human-Biometric Sensor Interaction (HBSI) model to signature as an exemplar of a behavioural biometric. Using observational data collected from a range of subjects, our study shows that usability issues can be identified specific to individual capture device technologies. While most interactions are successful, a range of common interaction errors need to be mitigated by design to reduce overall error rates