7,756 research outputs found
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
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
On Face Segmentation, Face Swapping, and Face Perception
We show that even when face images are unconstrained and arbitrarily paired,
face swapping between them is actually quite simple. To this end, we make the
following contributions. (a) Instead of tailoring systems for face
segmentation, as others previously proposed, we show that a standard fully
convolutional network (FCN) can achieve remarkably fast and accurate
segmentations, provided that it is trained on a rich enough example set. For
this purpose, we describe novel data collection and generation routines which
provide challenging segmented face examples. (b) We use our segmentations to
enable robust face swapping under unprecedented conditions. (c) Unlike previous
work, our swapping is robust enough to allow for extensive quantitative tests.
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure
the effect of intra- and inter-subject face swapping on recognition. We show
that our intra-subject swapped faces remain as recognizable as their sources,
testifying to the effectiveness of our method. In line with well known
perceptual studies, we show that better face swapping produces less
recognizable inter-subject results. This is the first time this effect was
quantitatively demonstrated for machine vision systems
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