1,269 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
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six
basic facial expressions given a neutral face image. We propose to exploit the
face geometry by modeling the facial landmarks motion as curves encoded as
points on a hypersphere. By proposing a conditional version of manifold-valued
Wasserstein generative adversarial network (GAN) for motion generation on the
hypersphere, we learn the distribution of facial expression dynamics of
different classes, from which we synthesize new facial expression motions. The
resulting motions can be transformed to sequences of landmarks and then to
images sequences by editing the texture information using another conditional
Generative Adversarial Network. To the best of our knowledge, this is the first
work that explores manifold-valued representations with GAN to address the
problem of dynamic facial expression generation. We evaluate our proposed
approach both quantitatively and qualitatively on two public datasets;
Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the
effectiveness of our approach in generating realistic videos with continuous
motion, realistic appearance and identity preservation. We also show the
efficiency of our framework for dynamic facial expressions generation, dynamic
facial expression transfer and data augmentation for training improved emotion
recognition models
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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
- …