2,252 research outputs found
Learn to synthesize and synthesize to learn
Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
3D Face Reconstruction from Light Field Images: A Model-free Approach
Reconstructing 3D facial geometry from a single RGB image has recently
instigated wide research interest. However, it is still an ill-posed problem
and most methods rely on prior models hence undermining the accuracy of the
recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI)
obtained from light field cameras and learn CNN models that recover horizontal
and vertical 3D facial curves from the respective horizontal and vertical EPIs.
Our 3D face reconstruction network (FaceLFnet) comprises a densely connected
architecture to learn accurate 3D facial curves from low resolution EPIs. To
train the proposed FaceLFnets from scratch, we synthesize photo-realistic light
field images from 3D facial scans. The curve by curve 3D face estimation
approach allows the networks to learn from only 14K images of 80 identities,
which still comprises over 11 Million EPIs/curves. The estimated facial curves
are merged into a single pointcloud to which a surface is fitted to get the
final 3D face. Our method is model-free, requires only a few training samples
to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single
light field images under varying poses, expressions and lighting conditions.
Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces
reconstruction errors by over 20% compared to recent state of the art
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