904 research outputs found
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map
is always incomplete. In this paper, we propose a framework for training Deep
Convolutional Neural Network (DCNN) to complete the facial UV map extracted
from in-the-wild images. To this end, we first gather complete UV maps by
fitting a 3D Morphable Model (3DMM) to various multiview image and video
datasets, as well as leveraging on a new 3D dataset with over 3,000 identities.
Second, we devise a meticulously designed architecture that combines local and
global adversarial DCNNs to learn an identity-preserving facial UV completion
model. We demonstrate that by attaching the completed UV to the fitted mesh and
generating instances of arbitrary poses, we can increase pose variations for
training deep face recognition/verification models, and minimise pose
discrepancy during testing, which lead to better performance. Experiments on
both controlled and in-the-wild UV datasets prove the effectiveness of our
adversarial UV completion model. We achieve state-of-the-art verification
accuracy, , under the CFP frontal-profile protocol only by combining
pose augmentation during training and pose discrepancy reduction during
testing. We will release the first in-the-wild UV dataset (we refer as WildUV)
that comprises of complete facial UV maps from 1,892 identities for research
purposes
Efficient and Private Federated Trajectory Matching
Federated Trajectory Matching (FTM) is gaining increasing importance in big
trajectory data analytics, supporting diverse applications such as public
health, law enforcement, and emergency response. FTM retrieves trajectories
that match with a query trajectory from a large-scale trajectory database,
while safeguarding the privacy of trajectories in both the query and the
database. A naive solution to FTM is to process the query through Secure
Multi-Party Computation (SMC) across the entire database, which is inherently
secure yet inevitably slow due to the massive secure operations. A promising
acceleration strategy is to filter irrelevant trajectories from the database
based on the query, thus reducing the SMC operations. However, a key challenge
is how to publish the query in a way that both preserves privacy and enables
efficient trajectory filtering. In this paper, we design GIST, a novel
framework for efficient Federated Trajectory Matching. GIST is grounded in
Geo-Indistinguishability, a privacy criterion dedicated to locations. It
employs a new privacy mechanism for the query that facilitates efficient
trajectory filtering. We theoretically prove the privacy guarantee of the
mechanism and the accuracy of the filtering strategy of GIST. Extensive
evaluations on five real datasets show that GIST is significantly faster and
incurs up to 3 orders of magnitude lower communication cost than the
state-of-the-arts.Comment: 14 page
- …