11,731 research outputs found
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
Deep networks trained on millions of facial images are believed to be closely
approaching human-level performance in face recognition. However, open world
face recognition still remains a challenge. Although, 3D face recognition has
an inherent edge over its 2D counterpart, it has not benefited from the recent
developments in deep learning due to the unavailability of large training as
well as large test datasets. Recognition accuracies have already saturated on
existing 3D face datasets due to their small gallery sizes. Unlike 2D
photographs, 3D facial scans cannot be sourced from the web causing a
bottleneck in the development of deep 3D face recognition networks and
datasets. In this backdrop, we propose a method for generating a large corpus
of labeled 3D face identities and their multiple instances for training and a
protocol for merging the most challenging existing 3D datasets for testing. We
also propose the first deep CNN model designed specifically for 3D face
recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our
test dataset comprises 1,853 identities with a single 3D scan in the gallery
and another 31K scans as probes, which is several orders of magnitude larger
than existing ones. Without fine tuning on this dataset, our network already
outperforms state of the art face recognition by over 10%. We fine tune our
network on the gallery set to perform end-to-end large scale 3D face
recognition which further improves accuracy. Finally, we show the efficacy of
our method for the open world face recognition problem.Comment: 11 page
Inactive or moderately active human promoters are enriched for inter-individual epialleles
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Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
Torque magnetometry study of magnetically ordered state and spin reorientation in the quasi-one-dimensional Heisenberg antiferromagnet CuSbO
We present an experimental study of macroscopic and microscopic magnetic
anisotropy of a spin tetramer system \cso using torque magnetometry and ESR
spectroscopy. Large rotation of macroscopic magnetic axes with temperature
observed from torque magnetometry agrees reasonably well with the rotation of
the tensor above ~K. Below 50~K, the
tensor is temperature independent, while macroscopic magnetic axes continue to
rotate. Additionally, the susceptibility anisotropy has a temperature
dependence which cannot be reconciled with the isotropic Heisenberg model of
interactions between spins. ESR linewidth analysis shows that anisotropic
exchange interaction must be present in \csos. These findings strongly support
the presence of anisotropic exchange interactions in the Hamiltonian of the
studied system. Below ~K, the system enters a long - range
antiferromagnetically ordered state with easy axis along the
direction. Small but significant rotation of magnetic axes is also observed in
the antiferromagnetically ordered state suggesting strong spin-lattice coupling
in this system.Comment: 10 pages, 10 figure
Congenital anomalies from a physics perspective. The key role of "manufacturing" volatility
Genetic and environmental factors are traditionally seen as the sole causes
of congenital anomalies. In this paper we introduce a third possible cause,
namely random "manufacturing" discrepancies with respect to ``design'' values.
A clear way to demonstrate the existence of this component is to ``shut'' the
two others and to see whether or not there is remaining variability. Perfect
clones raised under well controlled laboratory conditions fulfill the
conditions for such a test. Carried out for four different species, the test
reveals a variability remainder of the order of 10%-20% in terms of coefficient
of variation. As an example, the CV of the volume of E.coli bacteria
immediately after binary fission is of the order of 10%. In short,
``manufacturing'' discrepancies occur randomly, even when no harmful mutation
or environmental factors are involved. Not surprisingly, there is a strong
connection between congenital defects and infant mortality. In the wake of
birth there is a gradual elimination of defective units and this screening
accounts for the post-natal fall of infant mortality. Apart from this trend,
post-natal death rates also have humps and peaks associated with various
inabilities and defects.\qL In short, infant mortality rates convert the
case-by-case and mostly qualitative problem of congenital malformations into a
global quantitative effect which, so to say, summarizes and registers what goes
wrong in the embryonic phase. Based on the natural assumption that for simple
organisms (e.g. rotifers) the manufacturing processes are shorter than for more
complex organisms (e.g. mammals), fewer congenital anomalies are expected.
Somehow, this feature should be visible on the infant mortality rate. How this
conjecture can be tested is outlined in our conclusion.Comment: 43 pages, 9 figure
End-to-end 3D face reconstruction with deep neural networks
Monocular 3D facial shape reconstruction from a single 2D facial image has
been an active research area due to its wide applications. Inspired by the
success of deep neural networks (DNN), we propose a DNN-based approach for
End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
from recent works that reconstruct and refine the 3D face in an iterative
manner using both an RGB image and an initial 3D facial shape rendering, our
DNN model is end-to-end, and thus the complicated 3D rendering process can be
avoided. Moreover, we integrate in the DNN architecture two components, namely
a multi-task loss function and a fusion convolutional neural network (CNN) to
improve facial expression reconstruction. With the multi-task loss function, 3D
face reconstruction is divided into neutral 3D facial shape reconstruction and
expressive 3D facial shape reconstruction. The neutral 3D facial shape is
class-specific. Therefore, higher layer features are useful. In comparison, the
expressive 3D facial shape favors lower or intermediate layer features. With
the fusion-CNN, features from different intermediate layers are fused and
transformed for predicting the 3D expressive facial shape. Through extensive
experiments, we demonstrate the superiority of our end-to-end framework in
improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1
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