Facial Biometrics on Mobile Devices: Interaction and Quality Assessment

Biometric face recognition is a quick and convenient security method that allows unlocking a smartphone device without the need to remember a PIN code or a password. However, the unconstrained mobile environment brings considerable challenges in facial verification performance. Not only the verification but also the enrolment on the mobile device takes place in unpredictable surroundings. In particular, facial verification involves the enrolment of unsupervised users across a range of environmental conditions, light exposure, and additional variations in terms of user's poses and image background. Is there a way to estimate the variations that a mobile scenario introduces over the facial verification performance? A quality assessment can help in enhancing the biometric performance, but in the context of mobile devices, most of the standardised requirements and methodology presented are based on passport scenarios. A comprehensive analysis should be performed to assess the biometric performance in terms of image quality and user interaction in the particular context of mobile devices. This work aimed to contribute to improving the performance and the adaptability of facial verification systems implemented on smartphones. Fifty-three participants were asked to provide facial images suitable for face verification across several locations and scenarios. A minimum of 150 images per user was collected with a smartphone camera within three different sessions. Sensing data was recorded to assess user interaction during the biometric presentation. Images were also recorded using a Single Lens Reflex camera to enable a comparison with conditions similar to a passport scenario. Results showed the relationship within five selected quality metrics commonly used for quality assessment and the variables introduced by the environment, the user and the camera. Innovative methodologies were also proposed to assess the user interaction using sensors implemented in the smartphone. The analysis underlined important issues and formulated useful observations to enhance facial verification performance on smartphone devices

Interaction evaluation of a mobile voice authentication system

Biometric recognition is nowadays widely used in smartphones, making the users' authentication easier and more transparent than PIN codes or patterns. Starting from this idea, the EU project PIDaaS aims to create a secure authentication system through mobile devices based on voice and face recognition as two of the most reliable and user-accepted modalities. This work introduces the project and the first PIDaaS usability evaluation carried out by means of the well-known HBSI model In this experiment, participants interact with a mobile device using the PIDaaS system under laboratory conditions: video recorded and assisted by an operator. Our findings suggest variability among sessions in terms of usability and feed the next PIDaaS HCI design

Voice and face interaction evaluation of a mobile authentication platform

Biometric authentication in mobile devices has become a key aspect of application security. However, the use of dedicated sensors such as fingerprint/iris sensors may not always be feasible. As an alternative, the use of face and voice biometrics using the generic sensors integrated in smartphones is gaining momentum. This work applied the HBSI framework to analyise the user’s interaction with the mobile PIDaaS platform that integrates voice and face authentication. Our analysis enables a thorough comparison between the user’s interaction for these two modalities with the same population

Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.This work was supported by the SHAPES project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 857159, and in part by the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project ÉGIDA—RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD under Grant CER20191012

Sensing Movement on Smartphone Devices to Assess User Interaction for Face Verification

Unlocking and protecting smartphone devices has become easier with the introduction of biometric face verification, as it has the promise of a secure and quick authentication solution to prevent unauthorised access. However, there are still many challenges for this biometric modality in a mobile context, where the user’s posture and capture device are not constrained. This research proposes a method to assess user interaction by analysing sensor data collected in the background of smartphone devices during verification sample capture. From accelerometer data, we have extracted magnitude variations and angular acceleration for pitch, roll, and yaw (angles around the x-axis, y-axis, and z-axis of the smartphone respectively) as features to describe the amplitude and number of movements during a facial image capture process. Results obtained from this experiment demonstrate that it can be possible to ensure good sample quality and high biometric performance by applying an appropriate threshold that will regulate the amplitude on variations of the smartphone movements during facial image capture. Moreover, the results suggest that better quality images are obtained when users spend more time positioning the smartphone before taking an image

Environmental Effects on Face Recognition in Smartphones

Face recognition is convenient for user authentication on smartphones as it offers several advantages suitable for mobile environments. There is no need to remember a numeric code or password or carry tokens. Face verification allows the unlocking of the smartphone, pay bills or check emails through looking at the smartphone. However, devices mobility also introduces a lot of factors that may influence the biometric performance mainly regarding interaction and environment. Scenarios can vary significantly as there is no control of the surroundings. Noise can be caused by other people appearing on the background, by different illumination conditions, by different users’ poses and through many other reasons. User-interaction with biometric systems is fundamental: bad experiences may derive to unwillingness to use the technology. But how does the environment influence the quality of facial images? And does it influence the user experience with face recognition? In order to answer these questions, our research investigates the user-biometric system interaction from a non-traditional point of view: we recreate reallife scenarios to test which factors influence the image quality in face recognition and, quantifiably, to what extent. Results indicate the variability in face recognition performance when varying environmental conditions using smartphones

Biometric Systems Interaction Assessment: The State of the Art

The design and implementation of effective and efficient biometric systems presents a series of challenges to information technology (IT) designers to ensure robust performance. One of the most important factors across biometric systems, aside from algorithmic matching ability, is the human interaction influence on performance. Changes in biometric system paradigms have motivated further testing methods, especially within mobile environments, where the interaction with the device has fewer environmental constraints, whichmay severely affect system performance. Testing methods involve the need for reflecting on the influence of user-system interaction on the overall system performance in order to provide information for design and testing. This paper reflects on the state of the art of biometric systems interaction assessment, leading to a comprehensive document of the relevant research and standards in this area. Furthermore, the current challenges are discussed and thus we provide a roadmap for the future of biometrics systems interaction research

Biometrics: Accessibility challenge or opportunity?

Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease of use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario. The biometric authentication mechanisms used include face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern in order to check if biometric recognition is indeed a real improvement. The trial test subjects within this work were people with real-life accessibility concerns. A group of people without accessibility concerns also participated, providing a baseline performance. Experimental results are presented concerning performance, HCI (Human-Computer Interaction) and accessibility, grouped according to category of accessibility concern. Our results reveal links between individual modalities and user category establishing guidelines for future accessible biometric products

Attacking a smartphone biometric fingerprint system:a novice’s approach

Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe