1,790 research outputs found
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
In this paper, we propose a kinship generator network that can synthesize a
possible child face by analyzing his/her parent's photo. For this purpose, we
focus on to handle the scarcity of kinship datasets throughout the paper by
proposing novel solutions in particular. To extract robust features, we
integrate a pre-trained face model to the kinship face generator. Moreover, the
generator network is regularized with an additional face dataset and
adversarial loss to decrease the overfitting of the limited samples. Lastly, we
adapt cycle-domain transformation to attain a more stable results. Experiments
are conducted on Families in the Wild (FIW) dataset. The experimental results
show that the contributions presented in the paper provide important
performance improvements compared to the baseline architecture and our proposed
method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Each smile is unique: one person surely smiles in different ways (e.g.,
closing/opening the eyes or mouth). Given one input image of a neutral face,
can we generate multiple smile videos with distinctive characteristics? To
tackle this one-to-many video generation problem, we propose a novel deep
learning architecture named Conditional Multi-Mode Network (CMM-Net). To better
encode the dynamics of facial expressions, CMM-Net explicitly exploits facial
landmarks for generating smile sequences. Specifically, a variational
auto-encoder is used to learn a facial landmark embedding. This single
embedding is then exploited by a conditional recurrent network which generates
a landmark embedding sequence conditioned on a specific expression (e.g.,
spontaneous smile). Next, the generated landmark embeddings are fed into a
multi-mode recurrent landmark generator, producing a set of landmark sequences
still associated to the given smile class but clearly distinct from each other.
Finally, these landmark sequences are translated into face videos. Our
experimental results demonstrate the effectiveness of our CMM-Net in generating
realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Polarimetric thermal to visible face verification entails matching two images
that contain significant domain differences. Several recent approaches have
attempted to synthesize visible faces from thermal images for cross-modal
matching. In this paper, we take a different approach in which rather than
focusing only on synthesizing visible faces from thermal faces, we also propose
to synthesize thermal faces from visible faces. Our intuition is based on the
fact that thermal images also contain some discriminative information about the
person for verification. Deep features from a pre-trained Convolutional Neural
Network (CNN) are extracted from the original as well as the synthesized
images. These features are then fused to generate a template which is then used
for verification. The proposed synthesis network is based on the self-attention
generative adversarial network (SAGAN) which essentially allows efficient
attention-guided image synthesis. Extensive experiments on the ARL polarimetric
thermal face dataset demonstrate that the proposed method achieves
state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On
Biometrics (ICB 2019
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