12,688 research outputs found
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
A Decoupled 3D Facial Shape Model by Adversarial Training
Data-driven generative 3D face models are used to compactly encode facial
shape data into meaningful parametric representations. A desirable property of
these models is their ability to effectively decouple natural sources of
variation, in particular identity and expression. While factorized
representations have been proposed for that purpose, they are still limited in
the variability they can capture and may present modeling artifacts when
applied to tasks such as expression transfer. In this work, we explore a new
direction with Generative Adversarial Networks and show that they contribute to
better face modeling performances, especially in decoupling natural factors,
while also achieving more diverse samples. To train the model we introduce a
novel architecture that combines a 3D generator with a 2D discriminator that
leverages conventional CNNs, where the two components are bridged by a geometry
mapping layer. We further present a training scheme, based on auxiliary
classifiers, to explicitly disentangle identity and expression attributes.
Through quantitative and qualitative results on standard face datasets, we
illustrate the benefits of our model and demonstrate that it outperforms
competing state of the art methods in terms of decoupling and diversity.Comment: camera-ready version for ICCV'1
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