Investigating the Impact of Demographic Factors on Contactless Fingerprint Interoperability

Improvements in contactless fingerprinting have resulted in contactless fingerprints becoming a faster and more convenient alternative to contact fingerprints. The interoperability between contactless fingerprints and contact fingerprints and how demographic factors can change interoperability has been challenging since COVID-19; the need for hygienic alternatives has only grown because of the sudden focus during the pandemic. Past work has shown issues with the interoperability of contactless prints from kiosk devices and phone fingerprint collection apps. Demographic bias in photography for facial recognition could affect photographed fingerprints. The paper focuses on evaluating match performance between contact and contactless fingerprints and evaluating match score bias based on five skin demographics; melanin, erythema, and the three measurements of the CIELab color space. The interoperability of three fingerprint matchers was tested. The best and worst Area Under the Curve (AUC) and Equal Error Rate (EER) values for the best-performing matcher were an AUC of 0.99398 and 0.97873 and an EER of 0.03016 and 0.07555, respectively, while the best contactless AUC and EER were 0.99337 and 0.03387 indicating that contactless match performance can be as good as contact fingerprints depending on the device. In contrast, the best and worst AUC and EER for the cellphone contactless fingerprints were an AUC of 0.96812 and 0.85772 and an EER of 0.08699 and 0.22130, falling short of the lowest performing contact fingerprints. Demographic analysis was on the top two of the three matchers based on the top one percent of non-match scores. Resulting efforts found matcher-specific bias for melanin showing specific ranges affected by low and high melanin values. While higher levels of erythema and general redness of the skin improved performance. Higher lightness values showed a decreased performance in the top-performing matcher

De-identification for privacy protection in multimedia content : A survey

This document is the Accepted Manuscript version of the following article: Slobodan Ribaric, Aladdin Ariyaeeinia, and Nikola Pavesic, ‘De-identification for privacy protection in multimedia content: A survey’, Signal Processing: Image Communication, Vol. 47, pp. 131-151, September 2016, doi: https://doi.org/10.1016/j.image.2016.05.020. This manuscript version is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License CC BY NC-ND 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents.Peer reviewe