20,629 research outputs found
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
We propose a method for predicting the 3D shape of a deformable surface from
a single view. By contrast with previous approaches, we do not need a
pre-registered template of the surface, and our method is robust to the lack of
texture and partial occlusions. At the core of our approach is a {\it
geometry-aware} deep architecture that tackles the problem as usually done in
analytic solutions: first perform 2D detection of the mesh and then estimate a
3D shape that is geometrically consistent with the image. We train this
architecture in an end-to-end manner using a large dataset of synthetic
renderings of shapes under different levels of deformation, material
properties, textures and lighting conditions. We evaluate our approach on a
test split of this dataset and available real benchmarks, consistently
improving state-of-the-art solutions with a significantly lower computational
time.Comment: Accepted at CVPR 201
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
Despite significant recent advances in the field of head pose estimation and
facial expression recognition, raising the cognitive level when analysing human
activity presents serious challenges to current concepts. Motivated by the need
of generating comprehensible visual representations from different sets of
data, we introduce a system capable of monitoring human activity through head
pose and facial expression changes, utilising an affordable 3D sensing
technology (Microsoft Kinect sensor). An approach build on discriminative
random regression forests was selected in order to rapidly and accurately
estimate head pose changes in unconstrained environment. In order to complete
the secondary process of recognising four universal dominant facial expressions
(happiness, anger, sadness and surprise), emotion recognition via facial
expressions (ERFE) was adopted. After that, a lightweight data exchange format
(JavaScript Object Notation-JSON) is employed, in order to manipulate the data
extracted from the two aforementioned settings. Such mechanism can yield a
platform for objective and effortless assessment of human activity within the
context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016),
University of Essex, UK, 6 page
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