1,721,042 research outputs found
Biometric antispoofing on mobile devices
No full textIn the present chapter, after a thorough review of state-of-the-art in biometric antispoofing, we present a software-based spoof detection prototype for mobile devices, named MoBio_LivDet (Mobile Biometric Liveness Detection) that can be used in multiple biometric systems. MoBio_LivDet analyzes local features and global structures of face, iris and fingerprint biometric images using a set of low-level feature descriptors and decision-level fusion. In particular, we propose to use image descriptor classification algorithms Locally Uniform Comparison Image Descriptor (LUCID) [15], CENsus TRansform hISTogram (CENTRIST) [16] and Patterns of Oriented Edge Magnitudes (POEM) [17] for face, iris and fingerprint spoof detection. The proposed system allows user to choose “Security Level” (SL) against spoofing, between “low, " “medium” and “high.” Depending on SL, the system selects unitdescriptor or multidescriptors-fusion-based liveness detection. These descriptors are computationally inexpensive, fast and novel approach to real-time image description, which are desirable requisites for mobile processors. Experiments on publicly available data sets containing several real and spoofed faces, irises and fingerprints show promising results. Chapter Contents: • 15.1 Introduction • 15.2 Biometric antispoofing • 15.2.1 State-of-the-art in face antispoofing • 15.2.2 State-of-the-art in fingerprint antispoofing • 15.2.3 State-of-the-art in iris antispoofing • 15.3 Case study: MoBio_LivDet system • 15.3.1 Experiments • 15.4 Research opportunities • 15.4.1 Mobile liveness detection • 15.4.2 Mobile biometric spoofing databases • 15.4.3 Generalization to unknown attacks • 15.4.4 Randomizing input biometric data • 15.4.5 Fusion of biometric system and countermeasures • 15.5 Conclusion • References
Multitrait Selfie: Low-Cost Multimodal Smartphone User Authentication
No full textBiometric identification is biometric-based authentication on mobile devices that nowadays has become ubiquitous, especially in unattended (e.g., access control for banks) and consumer (e.g., mobile phone unlocking) applications. While, face, fingerprint and inherent behavioral biometrics using inbuilt sensors such as accelerometer for smartphones person authentication, they have yet not achieved the desired or required level of efficiency, security and usability. This chapter presents an uncontrolled multibiometric smartphone framework utilizing multitrait selfie and behavioral biometrics. In particular, the presented system authenticates subject by ocular and face selfie features. This new multimodal biometric system also takes silently into account micro-movements of the phone, movements of the user’s finger on the touchscreen while user is capturing the multitrait selfie and entering passcode simultaneously in a split-screen mode of the smartphone. Addition of micro-movements behaviors enhances not only the performance but also robustness against noise and spoofing attacks. For this study, we collected a mobile multimodal dataset (MultiTouchMove) of touchstroke and phone-movement patterns in the wild from 95 subjects, which is made publicly available by the authors. Preliminary experimental analysis, using public MOBIO Face, VISOB ocular, and MultiTouchMove mobile datasets, on accuracy and usability shows promising results
Robustness of Multi-modal Biometric Systems under Realistic Spoof Attacks against All Traits
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Robustness Analysis of Likelihood Ratio Score Fusion Rule for Multimodal Biometric Systems under Spoof Attacks
No full textRecent works have shown that, contrary to a common belief, multi-modal biometric systems may be “forced” by an impostor by submitting a spoofed biometric replica of a genuine user to only one of the matchers. Although those results were obtained under a worst-case scenario when the attacker is able to replicate the exact appearance of the true biometric, this raises the issue of investigating more thoroughly the robustness of multi-modal systems against spoof attacks and devising new methods to design robust systems against them. To this aim, in this paper we propose a robustness evaluation method which takes into account also scenarios more realistic than the worst-case one. Our method is based on an analytical model of the score distribution of fake traits, which is assumed to lie between the one of genuine and impostor scores, and is parametrised by a measure of the relative distance to the distribution of impostor scores, we name “fake strength”. Varying the value of such parameter allows one to simulate the different factors which can affect the distribution of fake scores, like the ability of the attacker to replicate a certain biometric. Preliminary experimental results on real bi-modal biometric data sets made up of faces and fingerprints show that the widely used LLR rule can be highly vulnerable to spoof attacks against one only matcher, even when the attack has a low fake strength
Mobile ocular biometrics in visible spectrum using local image descriptors: A preliminary study
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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