1,193 research outputs found
An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe
Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications
The unprecedented proliferation of smart devices together with novel
communication, computing, and control technologies have paved the way for the
Advanced Internet of Things~(A-IoT). This development involves new categories
of capable devices, such as high-end wearables, smart vehicles, and consumer
drones aiming to enable efficient and collaborative utilization within the
Smart City paradigm. While massive deployments of these objects may enrich
people's lives, unauthorized access to the said equipment is potentially
dangerous. Hence, highly-secure human authentication mechanisms have to be
designed. At the same time, human beings desire comfortable interaction with
their owned devices on a daily basis, thus demanding the authentication
procedures to be seamless and user-friendly, mindful of the contemporary urban
dynamics. In response to these unique challenges, this work advocates for the
adoption of multi-factor authentication for A-IoT, such that multiple
heterogeneous methods - both well-established and emerging - are combined
intelligently to grant or deny access reliably. We thus discuss the pros and
cons of various solutions as well as introduce tools to combine the
authentication factors, with an emphasis on challenging Smart City
environments. We finally outline the open questions to shape future research
efforts in this emerging field.Comment: 7 pages, 4 figures, 2 tables. The work has been accepted for
publication in IEEE Network, 2019. Copyright may be transferred without
notice, after which this version may no longer be accessibl
Face recognition with the RGB-D sensor
Face recognition in unconstrained environments is still a challenge, because of the many variations of the facial appearance due to changes in head pose, lighting conditions, facial expression, age, etc. This work addresses the problem of face recognition in the presence of 2D facial appearance variations caused by 3D head rotations. It explores the advantages of the recently developed consumer-level RGB-D cameras (e.g. Kinect). These cameras provide color and depth images at the same rate. They are affordable and easy to use, but the depth images are noisy and in low resolution, unlike laser scanned depth images. The proposed approach to face recognition is able to deal with large head pose variations using RGB-D face images. The method uses the depth information to correct the pose of the face. It does not need to learn a generic face model or make complex 3D-2D registrations. It is simple and fast, yet able to deal with large pose variations and perform pose-invariant face recognition. Experiments on a public database show that the presented approach is effective and efficient under significant pose changes. Also, the idea is used to develop a face recognition software that is able to achieve real-time face recognition in the presence of large yaw rotations using the Kinect sensor. It is shown in real-time how this method improves recognition accuracy and confidence level. This study demonstrates that RGB-D sensors are a promising tool that can lead to the development of robust pose-invariant face recognition systems under large pose variations
Smart speaker design and implementation with biometric authentication and advanced voice interaction capability
Advancements in semiconductor technology have reduced dimensions and cost
while improving the performance and capacity of chipsets. In addition,
advancement in the AI frameworks and libraries brings possibilities to
accommodate more AI at the resource-constrained edge of consumer IoT devices.
Sensors are nowadays an integral part of our environment which provide
continuous data streams to build intelligent applications. An example could be
a smart home scenario with multiple interconnected devices. In such smart
environments, for convenience and quick access to web-based service and
personal information such as calendars, notes, emails, reminders, banking, etc,
users link third-party skills or skills from the Amazon store to their smart
speakers. Also, in current smart home scenarios, several smart home products
such as smart security cameras, video doorbells, smart plugs, smart carbon
monoxide monitors, and smart door locks, etc. are interlinked to a modern smart
speaker via means of custom skill addition. Since smart speakers are linked to
such services and devices via the smart speaker user's account. They can be
used by anyone with physical access to the smart speaker via voice commands. If
done so, the data privacy, home security and other aspects of the user get
compromised. Recently launched, Tensor Cam's AI Camera, Toshiba's Symbio,
Facebook's Portal are camera-enabled smart speakers with AI functionalities.
Although they are camera-enabled, yet they do not have an authentication scheme
in addition to calling out the wake-word. This paper provides an overview of
cybersecurity risks faced by smart speaker users due to lack of authentication
scheme and discusses the development of a state-of-the-art camera-enabled,
microphone array-based modern Alexa smart speaker prototype to address these
risks
Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
Whole-body-based human authentication is a promising approach for remote
biometrics scenarios. Current literature focuses on either body recognition
based on RGB images or gait recognition based on body shapes and walking
patterns; both have their advantages and drawbacks. In this work, we propose
Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to
achieve more robust performances for indoor and outdoor whole-body based
recognition. Within DME, we propose GaitPattern, which is inspired by the
double helical gait pattern used in traditional gait analysis. The GaitPattern
contributes to robust identification performance over a large range of viewing
angles. Extensive experimental results on the CASIA-B dataset demonstrate that
the proposed method outperforms state-of-the-art recognition systems. We also
provide experimental results using the newly collected BRIAR dataset
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