283 research outputs found
Geometry-Aware Face Completion and Editing
Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked region. Firstly, a facial geometry estimator is learned to
estimate facial landmark heatmaps and parsing maps from the unmasked face
image. Then, an encoder-decoder structure generator serves to complete a face
image and disentangle its mask areas conditioned on both the masked face image
and the estimated facial geometry images. Besides, since low-rank property
exists in manually labeled masks, a low-rank regularization term is imposed on
the disentangled masks, enforcing our completion network to manage occlusion
area with various shape and size. Furthermore, our network can generate diverse
results from the same masked input by modifying estimated facial geometry,
which provides a flexible mean to edit the completed face appearance. Extensive
experimental results qualitatively and quantitatively demonstrate that our
network is able to generate visually pleasing face completion results and edit
face attributes as well
Attention-Set based Metric Learning for Video Face Recognition
Face recognition has made great progress with the development of deep
learning. However, video face recognition (VFR) is still an ongoing task due to
various illumination, low-resolution, pose variations and motion blur. Most
existing CNN-based VFR methods only obtain a feature vector from a single image
and simply aggregate the features in a video, which less consider the
correlations of face images in one video. In this paper, we propose a novel
Attention-Set based Metric Learning (ASML) method to measure the statistical
characteristics of image sets. It is a promising and generalized extension of
Maximum Mean Discrepancy with memory attention weighting. First, we define an
effective distance metric on image sets, which explicitly minimizes the
intra-set distance and maximizes the inter-set distance simultaneously. Second,
inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to
adapt set-aware global contents. Then ASML is naturally integrated into CNNs,
resulting in an end-to-end learning scheme. Our method achieves
state-of-the-art performance for the task of video face recognition on the
three widely used benchmarks including YouTubeFace, YouTube Celebrities and
Celebrity-1000.Comment: modify for ACP
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