3,775 research outputs found
Robust RGB-D Face Recognition Using Attribute-Aware Loss
Existing convolutional neural network (CNN) based face recognition algorithms
typically learn a discriminative feature mapping, using a loss function that
enforces separation of features from different classes and/or aggregation of
features within the same class. However, they may suffer from bias in the
training data such as uneven sampling density, because they optimize the
adjacency relationship of the learned features without considering the
proximity of the underlying faces. Moreover, since they only use facial images
for training, the learned feature mapping may not correctly indicate the
relationship of other attributes such as gender and ethnicity, which can be
important for some face recognition applications. In this paper, we propose a
new CNN-based face recognition approach that incorporates such attributes into
the training process. Using an attribute-aware loss function that regularizes
the feature mapping using attribute proximity, our approach learns more
discriminative features that are correlated with the attributes. We train our
face recognition model on a large-scale RGB-D data set with over 100K
identities captured under real application conditions. By comparing our
approach with other methods on a variety of experiments, we demonstrate that
depth channel and attribute-aware loss greatly improve the accuracy and
robustness of face recognition
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13
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Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
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