Multimodal biometrics offer superior matching performance over a single biometric. However real-life applications increasingly deploy biometric matching systems in non-ideal conditions where good image quality cannot be assured. We assess the impact of image quality on multimodal biometric recognition performance. Biometric recognition of face and iris is carried out on images while the image quality is varied. Image quality is controlled synthetically using defocus, motion blur, light intensity, contrast, resolution and color reduction. Results show that the chosen algorithm for iris recognition is highly sensitive to contrast while brightness has less influence. Effect of defocusing and motion blur on the performance is linear. The effect of pixel resolution is log-linear whereas color reduction has more pronounced effect at extreme values only. The face recognition algorithm is robust to the same variations even though the corresponding image quality followed the trends similar to that for the iris images. For face matching, quality was not a predictor of performance.

Image quality assessment and performance evaluation for multimodal biometric recognition using face and iris

Einhergehend mit der Digitalisierung vieler Lebensbereiche werden große Mengen persönlicher Daten von Konsument*innen durch Unternehmen und Institutionen erfasst und analysiert. Daher ist der verantwortungsvolle Umgang mit diesen Daten eines der drängendsten Themen der Gegenwart. Zwar gibt es zahlreiche gesetzliche Verordnungen (z. B. GDPR) sowie eine zunehmende Anzahl von Unternehmen, die sich freiwillig dem Datenschutz verpflichten, allerdings nutzen auch viele Unternehmen und Institutionen die Unachtsamkeit von Konsument*innen aus. Dies wird dadurch begünstigt, dass viele Konsument*innen zwar angeben, dass sie auf ihre Privatsphäre achten, aber nur wenige die dafür notwendigen Maßnahmen ergreifen. Diese Einstellungs-Verhaltens-Diskrepanz (Privatsphäre-Paradoxon) lässt sich einerseits durch ein rationales Kosten-Nutzen-Kalkül erklären, in dem Konsument*innen den Nutzen bestimmter Produkte (z. B. durch Personalisierung) mit der Preisgabe ihrer Daten verrechnen. Andererseits können situative Einflüsse (z. B. wenig Zeit) oder kognitive Verzerrungen (z. B. Kontrollillusion) Datenschutzbedenken in diesen Situationen verringern. Vor diesem Hintergrund führen wir in das Privatsphäre-Paradoxon ein und zeigen den Stand des Schrifttums auf, wobei wir auf situative und kognitiven Verzerrungen fokussieren. Abschließend wird das Konzept der drei Privatsphäre-Gaps eingeführt und ein Rahmen für zukünftige Forschung entwickelt.

/pdf/paradoxes-datenschutzverhalten-diskrepanz-zwischen-2vjxey27w7.pdf

Paradoxes Datenschutzverhalten: Diskrepanz zwischen Datenschutzbedenken und nachlässigem Umgang mit digitalen Dienstleistungen

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.

/pdf/an-assessment-of-the-usability-of-biometric-signature-2xliq1k7ht.pdf

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

Citation Hernández-Trapote, Álvaro (2010). Avances en el estudio, diseño y evaluación de sistemas interactivos multimodales en escenarios de seguridad biométrica. Thesis (Doctoral), E.T.S.I. Telecomunicación (UPM). https://doi.org/10.20868/UPM.thesis.6101. 

Avances en el estudio, diseño y evaluación de sistemas interactivos multimodales en escenarios de seguridad biométrica

Speaker clustering is the task of grouping a set of speech utterances into speaker-specific classes. The basic techniques for solving this task are similar to those used for speaker verification and identification. The hypothesis of this paper is that the techniques originally developed for speaker verification and identification are not sufficiently discriminative for speaker clustering. However, the processing chain for speaker clustering is quite large - there are many potential areas for improvement. The question is: where should improvements be made to improve the final result? To answer this question, this paper takes a biomimetic approach based on a study with human participants acting as an automatic speaker clustering system. Our findings are twofold: it is the stage of modeling that has the highest potential, and information with respect to the temporal succession of frames is crucially missing. Experimental results with our implementation of a speaker clustering system incorporating our findings and applying it on TIMIT data show the validity of our approach.

/pdf/unfolding-speaker-clustering-potential-a-biomimetic-approach-1iqynuhwte.pdf

Unfolding speaker clustering potential: a biomimetic approach

Biometric technologies are being increasingly deployed in practical applications but are currently mainly driven by governmental initiatives, ranging from border control applications to national ID programmes, with increasing social and legal impact on everyday's life. However, biometrics offer wider opportunities and their application as enabling technology for modern identity management systems, having a more user-centred approach, will be more important in the near future. In this paper we give an overview of the most important research topics for Biometrics and Identity Management for the near future.

Nineteen Urgent Research Topics in Biometrics and Identity Management

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Nineteen Urgent Research Topics in Biometrics and Identity Management