60,828 research outputs found
Robust Face Recognition by Constrained Part-based Alignment
Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a facial part, we develop in this paper a Constrained
Part-based Alignment (CPA) algorithm for face recognition across pose and/or
expression. Our proposed algorithm is based on a trainable CPA model, which
learns appearance evidence of individual parts and a tree-structured shape
configuration among different parts. Given a probe face, CPA simultaneously
aligns all its parts by fitting them to the appearance evidence with
consideration of the constraint from the tree-structured shape configuration.
This objective is formulated as a norm minimization problem regularized by
graph likelihoods. CPA can be easily integrated with many existing classifiers
to perform part-based face recognition. Extensive experiments on benchmark face
datasets show that CPA outperforms or is on par with existing methods for
robust face recognition across pose, expression, and/or illumination changes
Facial Landmark Detection: a Literature Survey
The locations of the fiducial facial landmark points around facial components
and facial contour capture the rigid and non-rigid facial deformations due to
head movements and facial expressions. They are hence important for various
facial analysis tasks. Many facial landmark detection algorithms have been
developed to automatically detect those key points over the years, and in this
paper, we perform an extensive review of them. We classify the facial landmark
detection algorithms into three major categories: holistic methods, Constrained
Local Model (CLM) methods, and the regression-based methods. They differ in the
ways to utilize the facial appearance and shape information. The holistic
methods explicitly build models to represent the global facial appearance and
shape information. The CLMs explicitly leverage the global shape model but
build the local appearance models. The regression-based methods implicitly
capture facial shape and appearance information. For algorithms within each
category, we discuss their underlying theories as well as their differences. We
also compare their performances on both controlled and in the wild benchmark
datasets, under varying facial expressions, head poses, and occlusion. Based on
the evaluations, we point out their respective strengths and weaknesses. There
is also a separate section to review the latest deep learning-based algorithms.
The survey also includes a listing of the benchmark databases and existing
software. Finally, we identify future research directions, including combining
methods in different categories to leverage their respective strengths to solve
landmark detection "in-the-wild"
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Practical face recognition has been studied in the past decades, but still
remains an open challenge. Current prevailing approaches have already achieved
substantial breakthroughs in recognition accuracy. However, their performance
usually drops dramatically if face samples are severely misaligned. To address
this problem, we propose a highly efficient misalignment-robust
locality-constrained representation (MRLR) algorithm for practical real-time
face recognition. Specifically, the locality constraint that activates the most
correlated atoms and suppresses the uncorrelated ones, is applied to construct
the dictionary for face alignment. Then we simultaneously align the warped face
and update the locality-constrained dictionary, eventually obtaining the final
alignment. Moreover, we make use of the block structure to accelerate the
derived analytical solution. Experimental results on public data sets show that
MRLR significantly outperforms several state-of-the-art approaches in terms of
efficiency and scalability with even better performance
Face Alignment Robust to Pose, Expressions and Occlusions
We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and reasons over binary occlusion labels. Our occlusion
reasoning proceeds by a hypothesize-and-test search over occlusion labels.
Hypotheses are generated by Constrained Local Model based shape fitting over
randomly sampled subsets of landmark detector responses and are evaluated by
the quality of face alignment. To span the entire range of facial pose and
expression variations we adopt an ensemble of independent Robust Constrained
Local Models to search over a discretized representation of pose and
expression. We perform extensive evaluation on a large number of face images,
both occluded and unoccluded. We find that our face alignment system trained
entirely on facial images captured "in-the-lab" exhibits a high degree of
generalization to facial images captured "in-the-wild". Our results are
accurate and stable over a wide spectrum of occlusions, pose and expression
variations resulting in excellent performance on many real-world face datasets
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
Face Alignment by Local Deep Descriptor Regression
We present an algorithm for extracting key-point descriptors using deep
convolutional neural networks (CNN). Unlike many existing deep CNNs, our model
computes local features around a given point in an image. We also present a
face alignment algorithm based on regression using these local descriptors. The
proposed method called Local Deep Descriptor Regression (LDDR) is able to
localize face landmarks of varying sizes, poses and occlusions with high
accuracy. Deep Descriptors presented in this paper are able to uniquely and
efficiently describe every pixel in the image and therefore can potentially
replace traditional descriptors such as SIFT and HOG. Extensive evaluations on
five publicly available unconstrained face alignment datasets show that our
deep descriptor network is able to capture strong local features around a given
landmark and performs significantly better than many competitive and
state-of-the-art face alignment algorithms
Face frontalization for Alignment and Recognition
Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a novel method for joint face landmark localization and frontal face
reconstruction (pose correction) using a small set of frontal images only. By
observing that the frontal facial image is the one with the minimum rank from
all different poses we formulate an appropriate model which is able to jointly
recover the facial landmarks as well as the frontalized version of the face. To
this end, a suitable optimization problem, involving the minimization of the
nuclear norm and the matrix norm, is solved. The proposed method is
assessed in frontal face reconstruction (pose correction), face landmark
localization, and pose-invariant face recognition and verification by
conducting experiments on facial images databases. The experimental results
demonstrate the effectiveness of the proposed method.Comment: 8 pages, 8 figure
Learning Deep Representation for Face Alignment with Auxiliary Attributes
In this study, we show that landmark detection or face alignment task is not
a single and independent problem. Instead, its robustness can be greatly
improved with auxiliary information. Specifically, we jointly optimize landmark
detection together with the recognition of heterogeneous but subtly correlated
facial attributes, such as gender, expression, and appearance attributes. This
is non-trivial since different attribute inference tasks have different
learning difficulties and convergence rates. To address this problem, we
formulate a novel tasks-constrained deep model, which not only learns the
inter-task correlation but also employs dynamic task coefficients to facilitate
the optimization convergence when learning multiple complex tasks. Extensive
evaluations show that the proposed task-constrained learning (i) outperforms
existing face alignment methods, especially in dealing with faces with severe
occlusion and pose variation, and (ii) reduces model complexity drastically
compared to the state-of-the-art methods based on cascaded deep model.Comment: to be published in the IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI
Deep Alignment Network: A convolutional neural network for robust face alignment
In this paper, we propose Deep Alignment Network (DAN), a robust face
alignment method based on a deep neural network architecture. DAN consists of
multiple stages, where each stage improves the locations of the facial
landmarks estimated by the previous stage. Our method uses entire face images
at all stages, contrary to the recently proposed face alignment methods that
rely on local patches. This is possible thanks to the use of landmark heatmaps
which provide visual information about landmark locations estimated at the
previous stages of the algorithm. The use of entire face images rather than
patches allows DAN to handle face images with large variation in head pose and
difficult initializations. An extensive evaluation on two publicly available
datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%.
Our method has also been submitted for evaluation as part of the Menpo
challenge.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop
(CVPRW) 201
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
Single-sample face recognition is one of the most challenging problems in
face recognition. We propose a novel algorithm to address this problem based on
a sparse representation based classification (SRC) framework. The new algorithm
is robust to image misalignment and pixel corruption, and is able to reduce
required gallery images to one sample per class. To compensate for the missing
illumination information traditionally provided by multiple gallery images, a
sparse illumination learning and transfer (SILT) technique is introduced. The
illumination in SILT is learned by fitting illumination examples of auxiliary
face images from one or more additional subjects with a sparsely-used
illumination dictionary. By enforcing a sparse representation of the query
image in the illumination dictionary, the SILT can effectively recover and
transfer the illumination and pose information from the alignment stage to the
recognition stage. Our extensive experiments have demonstrated that the new
algorithms significantly outperform the state of the art in the single-sample
regime and with less restrictions. In particular, the single-sample face
alignment accuracy is comparable to that of the well-known Deformable SRC
algorithm using multiple gallery images per class. Furthermore, the face
recognition accuracy exceeds those of the SRC and Extended SRC algorithms using
hand labeled alignment initialization
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