1,566 research outputs found
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
300 faces in-the-wild challenge: database and results
Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues. (a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low. (b) Most published works report experimental results using different training/testing sets, different error metrics and, of course, landmark points with semantically different locations. In this paper, we aim to overcome the aforementioned problems by (a) proposing a semi-automatic annotation technique that was employed to re-annotate most existing facial databases under a unified protocol, and (b) presenting the 300 Faces In-The-Wild Challenge (300-W), the first facial landmark localization challenge that was organized twice, in 2013 and 2015. To the best of our knowledge, this is the first effort towards a unified annotation scheme of massive databases and a fair experimental comparison of existing facial landmark localization systems. The images and annotations of the new testing database that was used in the 300-W challenge are available from http://ibug.doc.ic.ac.uk/resources/facial-point-annotations
Fully automated landmarking and facial segmentation on 3D photographs
Three-dimensional facial stereophotogrammetry provides a detailed
representation of craniofacial soft tissue without the use of ionizing
radiation. While manual annotation of landmarks serves as the current gold
standard for cephalometric analysis, it is a time-consuming process and is
prone to human error. The aim in this study was to develop and evaluate an
automated cephalometric annotation method using a deep learning-based approach.
Ten landmarks were manually annotated on 2897 3D facial photographs by a single
observer. The automated landmarking workflow involved two successive
DiffusionNet models and additional algorithms for facial segmentation. The
dataset was randomly divided into a training and test dataset. The training
dataset was used to train the deep learning networks, whereas the test dataset
was used to evaluate the performance of the automated workflow. The precision
of the workflow was evaluated by calculating the Euclidean distances between
the automated and manual landmarks and compared to the intra-observer and
inter-observer variability of manual annotation and the semi-automated
landmarking method. The workflow was successful in 98.6% of all test cases. The
deep learning-based landmarking method achieved precise and consistent landmark
annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the
inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The
Euclidean distance between the automated and manual landmarks was within 2 mm
in 69%. Automated landmark annotation on 3D photographs was achieved with the
DiffusionNet-based approach. The proposed method allows quantitative analysis
of large datasets and may be used in diagnosis, follow-up, and virtual surgical
planning.Comment: 13 pages, 4 figures, 7 tables, repository
https://github.com/rumc3dlab/3dlandmarkdetection
Unsupervised Performance Analysis of 3D Face Alignment
We address the problem of analyzing the performance of 3D face alignment
(3DFA) algorithms. Traditionally, performance analysis relies on carefully
annotated datasets. Here, these annotations correspond to the 3D coordinates of
a set of pre-defined facial landmarks. However, this annotation process, be it
manual or automatic, is rarely error-free, which strongly biases the analysis.
In contrast, we propose a fully unsupervised methodology based on robust
statistics and a parametric confidence test. We revisit the problem of robust
estimation of the rigid transformation between two point sets and we describe
two algorithms, one based on a mixture between a Gaussian and a uniform
distribution, and another one based on the generalized Student's
t-distribution. We show that these methods are robust to up to 50% outliers,
which makes them suitable for mapping a face, from an unknown pose to a frontal
pose, in the presence of facial expressions and occlusions. Using these methods
in conjunction with large datasets of face images, we build a statistical
frontal facial model and an associated parametric confidence metric, eventually
used for performance analysis. We empirically show that the proposed pipeline
is neither method-biased nor data-biased, and that it can be used to assess
both the performance of 3DFA algorithms and the accuracy of annotations of face
datasets
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
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