3,780 research outputs found
A statistical shape model for deformable surface
This short paper presents a deformable surface registration scheme which is based on the statistical shape
modelling technique. The method consists of two major processing stages, model building and model
fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and
matched to the new data by a modified iterative closest point (ICP) registration process. The proposed
method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can
achieve a reasonable result on surface registration, and can be used for patient position monitoring in
radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and
neck patients by analysis of facial articulation
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table
Pose-Invariant 3D Face Alignment
Face alignment aims to estimate the locations of a set of landmarks for a
given image. This problem has received much attention as evidenced by the
recent advancement in both the methodology and performance. However, most of
the existing works neither explicitly handle face images with arbitrary poses,
nor perform large-scale experiments on non-frontal and profile face images. In
order to address these limitations, this paper proposes a novel face alignment
algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for
a face image with an arbitrary pose. By integrating a 3D deformable model, a
cascaded coupled-regressor approach is designed to estimate both the camera
projection matrix and the 3D landmarks. Furthermore, the 3D model also allows
us to automatically estimate the 2D landmark visibilities via surface normals.
We gather a substantially larger collection of all-pose face images to evaluate
our algorithm and demonstrate superior performances than the state-of-the-art
methods
End-to-end 3D face reconstruction with deep neural networks
Monocular 3D facial shape reconstruction from a single 2D facial image has
been an active research area due to its wide applications. Inspired by the
success of deep neural networks (DNN), we propose a DNN-based approach for
End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
from recent works that reconstruct and refine the 3D face in an iterative
manner using both an RGB image and an initial 3D facial shape rendering, our
DNN model is end-to-end, and thus the complicated 3D rendering process can be
avoided. Moreover, we integrate in the DNN architecture two components, namely
a multi-task loss function and a fusion convolutional neural network (CNN) to
improve facial expression reconstruction. With the multi-task loss function, 3D
face reconstruction is divided into neutral 3D facial shape reconstruction and
expressive 3D facial shape reconstruction. The neutral 3D facial shape is
class-specific. Therefore, higher layer features are useful. In comparison, the
expressive 3D facial shape favors lower or intermediate layer features. With
the fusion-CNN, features from different intermediate layers are fused and
transformed for predicting the 3D expressive facial shape. Through extensive
experiments, we demonstrate the superiority of our end-to-end framework in
improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS
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
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
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