14,609 research outputs found
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Existing single view, 3D face reconstruction methods can produce beautifully
detailed 3D results, but typically only for near frontal, unobstructed
viewpoints. We describe a system designed to provide detailed 3D
reconstructions of faces viewed under extreme conditions, out of plane
rotations, and occlusions. Motivated by the concept of bump mapping, we propose
a layered approach which decouples estimation of a global shape from its
mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which
acts as a foundation and then separately layer this foundation with details
represented by a bump map. We show how a deep convolutional encoder-decoder can
be used to estimate such bump maps. We further show how this approach naturally
extends to generate plausible details for occluded facial regions. We test our
approach and its components extensively, quantitatively demonstrating the
invariance of our estimated facial details. We further provide numerous
qualitative examples showing that our method produces detailed 3D face shapes
in viewing conditions where existing state of the art often break down.Comment: Accepted to CVPR'18. Previously titled: "Extreme 3D Face
Reconstruction: Looking Past Occlusions
Face recognition technologies for evidential evaluation of video traces
Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
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