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Research
Highlights |
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·
Survey on 3D face reconstruction from
uncalibrated images, Computer Science Review, 2021. ·
3D Fetal Face Reconstruction from Ultrasound
Imaging, VISAPP 2021. ·
An Enhanced Adversarial Network with Combined
Latent Features for Spatio-Temporal Facial Affect
Estimation in the Wild, VISAPP 2021. ·
Spectral Correspondence Framework for
Building a 3D Baby Face Model, FG 2020. ·
Refining the resolution of craniofacial
dysmorphology in bipolar disorder as an index of brain dysmorphogenesis,
Psychiatry Research, 291: 113243, 2020. ·
Cogans For Unsupervised Visual Speech
Adaptation to New Speakers, ICASSP 2020. ·
Tensor Decomposition and Non-linear Manifold
Modeling for 3D Head Pose Estimation, International Journal of
Computer Vision, 127(10): 1565–1585, 2019. ·
Three-Dimensional Face Reconstruction from
Uncalibrated Photographs: Application to Early Detection of Genetic Syndromes,
MICCAI CLIP 2019. ·
Robust facial alignment with internal
denoising auto-encoder, CRV 2019. ·
Lip-Reading with Limited-Data Network,
EUSIPCO 2019. ·
Fully end-to-end composite recurrent
convolution network for deformable facial tracking in the wild, FG
2019. ·
Automatic local shape spectrum analysis for
3D facial expression recognition, Image and Vision Computing, 79:
86–98, 2018. ·
3D head pose estimation using tensor
decomposition and non-linear manifold modeling, 3DV 2018. ·
Survey on Automatic Lip-Reading in the Era of
Deep Learning, Image and Vision Computing, 78: 53–72, 2018. ·
A quantitative comparison
of methods for 3D face reconstruction from 2D images, FG 2018. ·
The
Visual Lip Reading Feasibility Database –
Freely available for research. ·
Head
Pose Estimation Based on 3-D Facial Landmarks Localization and Regression,
FG 2017 –
Winner of the Head Pose Estimation Challenge. ·
Local Shape
Spectrum Analysis for 3D Facial Expression Recognition, FG 2017. ·
Fusion of
Valence and Arousal Annotations through Dynamic Subjective Ordinal Modelling,
FG 2017 ·
Automatic
Viseme Vocabulary Construction to Enhance Continuous Lip-reading, VISAPP
2017 ·
SRILF
3D Face Landmarker –
Free research software, v1.0, 2016. ·
A
multimodal annotation schema for non-verbal affective analysis in the
health-care domain, MARMI 2016 ·
On the
Quantitative Analysis of Craniofacial Asymmetry in 3D, FG 2015 ·
3D
Facial Landmark Localization with Asymmetry Patterns and Shape Regression
from Incomplete Local Features, IEEE Trans on Cybernetics, 45 (9):
1717-1730, 2015. ·
Asymmetry
Patterns Shape Contexts to Describe the 3D Geometry of Craniofacial Landmarks,
Communications in Computer and Information Science, 458:19-35, 2014. ·
Live 3D
facial scanning and landmark detection using Shape Regression with Incomplete
Local Features, FG 2013 ·
Compensating
inaccurate annotations to train 3D facial landmark localization models,
Proc. Workshop on 3D Face Biometrics 2013 |
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Rotationally
Invariant 3D Shape Contexts Using Asymmetry Patterns, Proc. GRAPP 2013 – Best paper award |
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Most of the activity in
2018 – 2020 is within this project |
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The UNFACE
project addresses fine grained facial analysis with the goal of decoding
hidden facial information. The human face is a fundamental source of
information to understand the behavior of individuals. Traditionally this has
been exploited in computer vision for the recognition of identity and
expressions but it has been recently suggested that the information that
could be extracted from the face goes well beyond this and can be indicative
of things such as deception, heart rate, psychological states or even
psychiatric disorders such as autism or depression. Some of this information, however, might be not
apparent or it might even be hidden to us, and it could only be recovered by
means of specialized techniques. An iconic example is the detection of
cardiac heart rate by amplifying the subtle color changes of the face due to
the blood flow, which are invisible to the human eye. For more information,
please visit the project page |
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Most of the research from our group during the
period between 2018 – 2020 is framed within the UNFACE project, which has
directly or indirectly contributed to numerous results, as summarized below: ·
12 papers in conference
proceedings |
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Head Pose Estimation Based
on 3-D Facial Landmarks Localization and Regression |
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D. Derkach, A. Ruiz and F.M. Sukno FG 2017 Workshop on Dominant and Complementary Emotion Recognition
Using Micro Emotion Features and Head-Pose Estimation, Washington,
DC, USA, 2017. |
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In this paper we present a system that is able to
estimate head pose using only depth information from consumer RGB-D cameras
such as Kinect 2. In contrast to most approaches addressing this problem, we
do not rely on tracking and produce pose estimation in terms of pitch, yaw
and roll angles using single depth frames as input. Our system combines three
different methods for pose estimation: two of them are based on
state-of-the-art landmark detection and the third one is a dictionary-based
approach that is able to work in especially challenging scans where landmarks
or mesh correspondences are too difficult to obtain. We evaluated our system on the SASE database, which
consists of ~30K frames from 50 subjects. We obtained average pose estimation
errors between 5 and 8 degrees per angle, achieving the best performance in
the FG2017 Head Pose Estimation Challenge. Full code of the developed system
is available on-line. |
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Local Shape Spectrum
Analysis for 3D Facial Expression Recognition |
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D. Derkach and F.M. Sukno 12th IEEE International Conference on Face
and Gesture Recognition, Washington, DC, USA, 2017. |
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We investigate the problem of facial expression
recognition using 3D data. Building from one of the most successful
frameworks for facial analysis using exclusively 3D geometry, we extend the
analysis from a curve-based representation into a spectral representation,
which allows a complete description of the underlying surface that can be
further tuned to the desired level of detail. Spectral representations are
based on the decomposition of the geometry in its spatial frequency
components, much like a Fourier transform, which are related to intrinsic
characteristics of the surface. In this work, we propose the use of Graph
Laplacian Features (GLF), which results from the projection of local surface
patches into a common basis obtained from the Graph Laplacian eigenspace. We test the proposed approach in the BU-3DFE
database in terms of expressions and Action Units recognition. Our results
confirm that the proposed GLF produces consistently higher recognition rates
than the curves-based approach, thanks to a more complete description of the
surface, while requiring a lower computational complexity. We also show that
the GLF outperform the most popular alternative approach for spectral
representation, Shape-DNA, which is based on the Laplace Beltrami Operator and
cannot provide a stable basis that guarantee that the extracted signatures
for the different patches are directly comparable. |
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Manual AU annotations for high-intensity expressions in the
BU-3DFE database |
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A.
Fernandez-Lopez, O. Martinez and F.M. Sukno 12th IEEE International Conference on Face
and Gesture Recognition, Washington, DC, USA, 2017. |
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Speech is the most used communication method between
humans and it involves the perception of auditory and visual channels.
Automatic speech recognition focuses on interpreting the audio signals,
although the video can provide information that is complementary to the
audio. Exploiting the visual information, however, has proven challenging. On
one hand, researchers have reported that the mapping between phonemes and
visemes (visual units) is one-to-many because there are phonemes which are
visually similar and indistinguishable between them. On the other hand, it is
known that some people are very good lip-readers (e.g:
deaf people). We study the limit of visual only speech recognition in
controlled conditions. With this goal, we designed a new database in which
the speakers are aware of being read and aim to facilitate lip-reading. In the literature, there are discrepancies on whether
hearingimpaired people are better lip-readers than
normal-hearing people. Then, we analyze if there are differences between the
lip-reading abilities of 9 hearing-impaired and 15 normalhearing
people. Finally, human abilities are compared with the performance of a
visual automatic speech recognition system. In our tests, hearing-impaired
participants outperformed the normal-hearing participants but without
reaching statistical significance. Human observers were able to decode 44% of
the spoken message. In contrast, the visual only automatic system achieved
20% of word recognition rate. However, if we repeat the comparison in terms
of phonemes both obtained very similar recognition rates, just above 50%.
This suggests that the gap between human lip-reading and automatic
speechreading might be more related to the use of context than to the ability
to interpret mouth appearance. |
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List of 500
phonetically balanced sentences used in the database |
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Fusion of Valence and
Arousal Annotations through Dynamic Subjective Ordinal Modelling |
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A. Ruiz, O. Martinez,
X. Binefa and F.M. Sukno 12th IEEE International Conference on Face
and Gesture Recognition, Washington, DC, USA, 2017. |
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An essential issue when training and validating
computer vision systems for affect analysis is how to obtain reliable
ground-truth labels from a pool of subjective annotations. In this paper, we
address this problem when labels are given in an ordinal scale and annotated
items are structured as temporal sequences. This problem is of special
importance in affective computing, where collected data is typically formed
by videos of human interactions annotated according to the Valence and
Arousal (V-A) dimensions. Moreover, recent works have shown that inter-observer
agreement of V-A annotations can be considerably improved if these are given
in a discrete ordinal scale. In this context, we propose a novel framework
which explicitly introduces ordinal constraints to model the subjective
perception of annotators. We also incorporate dynamic information to take
into account temporal correlations between ground-truth labels. In our
experiments over synthetic and real data with V-A annotations, we show that
the proposed method outperforms alternative approaches which do not take into
account either the ordinal structure of labels or their temporal correlation. |
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Automatic Viseme Vocabulary
Construction to Enhance Continuous Lip-reading |
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A.
Fernandez-Lopez and F.M. Sukno 12th International Conference on Computer Vision Theory and
Applications, Porto, Portugal, 2017. |
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Speech is the most common communication method
between humans and involves the perception of both auditory and visual
channels. Automatic speech recognition focuses on interpreting the audio
signals, but it has been demonstrated that video can provide information that
is complementary to the audio. Thus, the study of automatic lip-reading is
important and is still an open problem. One of the key challenges is the
definition of the visual elementary units (the visemes) and their vocabulary.
Many researchers have analyzed the importance of the phoneme to viseme
mapping and have proposed viseme vocabularies with lengths between 11 and 15
visemes. These viseme vocabularies have usually been manually defined by
their linguistic properties and in some cases using decision trees or
clustering techniques. In this work, we focus on the automatic construction
of an optimal viseme vocabulary based on the association of phonemes with
similar appearance. To this end, we construct an automatic system that uses
local appearance descriptors to extract the main characteristics of the mouth
region and HMMs to model the statistic relations of both viseme and phoneme
sequences. To compare the performance of the system different descriptors
(PCA, DCT and SIFT) are analyzed. We test our system in a Spanish corpus of
continuous speech. Our results indicate that we are able to recognize
approximately 58% of the visemes, 47% of the phonemes and 23% of the words in
a continuous speech scenario and that the optimal viseme vocabulary for
Spanish is composed by 20 visemes.. |
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A multimodal annotation
schema for non-verbal affective analysis in the health-care domain |
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F.M. Sukno, M. Domínguez,
A. Ruiz, D. Schiller, F. Lingenfelser, L. Pragst, E. Kamateri and S. Vrochidis 1st International Workshop on Multimedia
Analysis and Retrieval for Multimodal Interaction, New York,
USA, pp. 9–14, 2016. |
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The development of conversational agents with human
interaction capabilities requires advanced affective state recognition
integrating non-verbal cues from the different modalities constituting what
in human communication we perceive as an overall affective state. Each of the
modalities is often handled by a different subsystem that conveys only a
partial interpretation of the whole and, as such, is evaluated only in terms
of its partial view. To tackle this shortcoming, we investigate the generation
of a unified multimodal annotation schema of non-verbal cues from the
perspective of an inter-disciplinary group of experts. We aim at obtaining a
common ground-truth with a unique representation using the Valence and
Arousal space and a discrete non-linear scale of values. The proposed
annotation schema is demonstrated on a corpus in the health-care domain but
is scalable to other purposes. Preliminary results on inter-rater variability
show a positive correlation of consensus level with high (absolute) values of
Valence and Arousal as well as with the number of annotators labeling a given
video sequence. |
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On the Quantitative
Analysis of Craniofacial
Asymmetry in 3D |
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F.M. Sukno,
M.A. Rojas, J.L. Waddington and P.F. Whelan 11th IEEE International Conference on Face
and Gesture Recognition, Ljubljana, Slovenia, 2015. |
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We address a systematic evaluation of facial
asymmetry from a population of 100 high-quality laser scans, which are first
symmetrized and then manipulated to introduce 25 synthetic patterns with a
variety of asymmetries. A quantitative evaluation is performed by comparing
these known asymmetries with those estimated by different automatic
algorithms. Estimation of the actual asymmetries present in the original
surface was also addressed. We find
that widely used methods based on least-squares minimization not only fail to
produce accurate estimates but, in some cases, recover asymmetry patterns
that are radically different from the actual asymmetry of the input surfaces,
with low or even negative correlation coefficients. A number of alternative
algorithms are tested, including landmark-, midline- and surface-based
approaches. Among these, we find that the best performance is obtained by a
hybrid approach combining surface and midline points, framed within a least
median of squares algorithm with weights that decay exponentially with the
distance from the midline and an additional term to ensure that the recovered
pattern of asymmetry is itself symmetric. |
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F.M. Sukno,
J.L. Waddington and P.F. Whelan IEEE Transactions on Cybernetics, 45(9):
1717–1730, 2015. |
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We
present a method for the automatic localization of facial landmarks that
integrates non-rigid deformation with the ability to handle missing points.
The algorithm generates sets of candidate locations from feature detectors
and performs combinatorial search constrained by a flexible shape model. A
key assumption of our approach is that for some landmarks there might not be
an accurate candidate in the input set. This is tackled by detecting partial
subsets of landmarks and inferring those that are missing, so that the
probability of the flexible model is maximized. The ability of the model to
work with incomplete information makes it possible to limit the number of
candidates that need to be retained, drastically reducing the number of
combinations to be tested with respect to the alternative of trying to always
detect the complete set of landmarks. We
demonstrate the accuracy of the proposed method in the Face Recognition Grand
Challenge (FRGC) database, where we obtain average errors of approximately
3.5 mm when targeting 14 prominent facial landmarks. For the majority of
these our method produces the most accurate results reported to date in this
database. Handling of occlusions and surfaces with missing parts is
demonstrated with tests on the Bosphorus database,
where we achieve an overall error of 4.81 mm and 4.25 mm for data with and
without occlusions, respectively. To investigate potential limits in the
accuracy that could be reached, we also report experiments on a database of
144 facial scans acquired in the context of clinical research, with manual
annotations performed by experts, where we obtain an overall error of 2.3 mm,
with averages per landmark below 3.4 mm for all 14 targeted points and within
2 mm for half of them. The coordinates of automatically located landmarks are
made available
on-line. |
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Live 3D facial scanning and
landmark detection using Shape Regression with Incomplete Local Features |
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F.M. Sukno,
J.L. Waddington and P.F. Whelan 10th IEEE International Conference on Face and Gesture
Recognition, Shanghai, China, 2013. |
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This
demo is focused on the automatic detection of facial landmarks in surfaces
obtained from a hand held laser scanner. The objective is to demonstrate the
effectiveness of the algorithm by detecting the landmarks on the facial
surface of any person that volunteers to be scanned. A hand
held laser scanner allows acquisition of a 3D surface by gathering
measurements made by sweeping the scanning wand over an object (in a manner
similar to spray painting). The final surface is obtained by merging the
different sweeps which can be from various viewpoints, allowing a complete
reconstruction of the facial surface, irrespective of the head pose and
possible self-occlusions. For this demo we use a Cobra Wand 298, Polhemus
FastSCANTM, Colchester, VT, USA).
The reconstruction from multiple viewpoints, together with portability and
price, are important advantages with respect to single-view scanners. Landmark
localization is accomplished by using SRILF (Shape
Regression with Incomplete Features). This algorithm works by calculating a set
of candidate points for each landmark and performing combinatorial search,
with the key assumption that some landmarks might be missed (i.e. no
candidates detected) which is tackled by using partial subsets of landmarks
and inferring those that are missing by maximizing their plausibility based
on a statistical shape model. Such assumption is crucial for the
generalizability of the model for live scanning scenarios, where
pre-processing is not possible but to a minimum extent and the quality of the
resulting surfaces can vary considerably. |
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Compensating inaccurate annotations to train 3D
facial landmark localization models |
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F.M. Sukno,
J.L. Waddington and P.F. Whelan Proc. FG Workshop on 3D Face Biometrics, Shanghai,
China, pp 1-8, 2013. |
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In this paper we investigate the impact
of inconsistency in manual annotations when they are used to train automatic
models for 3D facial landmark localization. We start by showing that it is
possible to objectively measure the consistency of annotations in a database,
provided that it contains replicates (i.e. repeated scans from the same
person). Applying such measure to the widely used FRGC database we find that
manual annotations currently available are suboptimal and can strongly impair
the accuracy of automatic models learnt therefrom. To address this issue, we present a
simple algorithm to automatically correct a set of annotations and show that
it can help to significantly improve the accuracy of the models in terms of
landmark localization errors. This improvement is observed even when errors
are measured with respect to the original (not corrected) annotations.
However, we also show that if errors are computed against an alternative set
of manual annotations with higher consistency, the accuracy of the models
constructed using the corrections from the presented algorithm tends to
converge to the one achieved by building the models on the alternative, more
consistent set. |
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Asymmetry
Patterns Shape Contexts to Describe the 3D Geometry of Craniofacial Landmarks |
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F.M. Sukno,
J.L. Waddington and P.F. Whelan Proc. 8th International Conference on Computer Graphics
Theory and Applications, Barcelona,
Spain, 2013. Computer Vision, Imaging and Computer Graphics -- Theory and
Application, Volume 458, pp 19-3, 2014. |
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We present a new family of 3D geometry
descriptors based on asymmetry patterns from the popular 3D Shape Contexts
(3DSC). Our approach resolves the azimuth ambiguity of 3DSC, thus providing
rotational invariance, at the expense of a marginal increase in computational
load, outperforming previous algorithms dealing with azimuth ambiguity. We build on a recently presented measure
of approximate rotational symmetry in 2D, defined as the overlapping area
between a shape and rotated versions of itself, to extract asymmetry patterns
from a 3DSC in a variety of ways, depending on the spatial relationships that
need to be highlighted or disabled. Thus, we define Asymmetry Patterns Shape
Contexts (APSC) from a subset of the possible spatial relations present in the
spherical grid of 3DSC; hence they can be thought of as a family of
descriptors that depend on the subset that is selected. The possibility to
define APSC descriptors by selecting diverse spatial patterns from a 3DSC has
two important advantages: (1) choosing the appropriate spatial patterns can
considerably reduce the errors obtained with 3DSC when targeting specific
types of points; (2) Once one APSC descriptor is built, additional ones can
be built with only incremental cost. Therefore, it is possible to use a pool
of APSC descriptors to maximize accuracy without a large increase in
computational cost. |
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3D
Facial Landmark Localization using Combinatorial Search and Shape Regression |
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F.M. Sukno, J.L. Waddington and P.F. Whelan Proc. 5th ECCV Workshop on Non-Rigid Shape Analysis and Deformable Image
Alignment, Firenze, Italy, LNCS vol. 7583, pp 32-41, 2012. |
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This paper presents a method for the
automatic detection of facial landmarks. The algorithm receives a set of 3D
candidate points for each landmark (e.g. from a feature detector) and
performs combinatorial search constrained by a deformable shape model. A key assumption of our approach is that
for some landmarks there might not be an accurate candidate in the input set.
This is tackled by detecting partial subsets of landmarks and inferring those
that are missing so that the probability of the deformable model is
maximized. The ability of the model to work with incomplete information makes
it possible to limit the number of candidates that need to be retained,
substantially reducing the number of possible combinations to be tested with
respect to the alternative of trying to always detect the complete set of
landmarks. We demonstrate the accuracy of the
proposed method in a set of 144 facial scans acquired by means of a hand-held
laser scanner in the context of clinical craniofacial dysmorphology research.
Using spin images to describe the geometry and targeting 11 facial landmarks,
we obtain an average error below 3 mm, which compares favorably with other
state of the art approaches based on geometric descriptors. |
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Comparing 3D
descriptors for local search of craniofacial landmarks |
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F.M. Sukno, J.L. Waddington and P.F. Whelan Proc. 8th International Symposium on Visual Computing, Rethymnon, Crete, Greece, LNCS vol. 7432, pp 92-103,
2012. |
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This paper presents a comparison of local
descriptors for a set of 26 craniofacial landmarks annotated on 144 scans
acquired in the context of clinical research. We focus on the accuracy of the
different descriptors on a per-landmark basis when constrained to a local
search. For most descriptors, we find that the curves of expected error
against the search radius have a plateau that can be used to characterize
their performance, both in terms of accuracy and maximum usable range for the
local search. Six histograms-based descriptors
were evaluated: three describing distances and three describing orientations.
No descriptor dominated over the rest and the best accuracy per landmark was
strongly distributed among 3 of the 6 algorithms evaluated. Ordering the
descriptors by average error (over all landmarks) did not coincide with the
ordering by most frequently selected, indicating that a comparison of
descriptors based on their global behavior might be misleading when targeting
facial landmarks. |
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F.M. Sukno,
T.A. Chowdhury, J.L. Waddington and P.F. Whelan Proc. Irish Machine Vision and Image Processing
Conference, Dublin, Ireland, pp 28-33, 2011. |
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This work addresses the localization of
11 prominent facial landmarks in 3D by fitting state of the art shape models
to 2D data. Quantitative results are provided for 34 scans at high resolution
(texture maps of 10 M-pixels) in terms of accuracy (with respect to manual
measurements) and precision (repeatability on different images from the same
individual). We obtain an average accuracy of
approximately 3 mm, and median repeatability of inter-landmark distances
typically below 2 mm, which are values comparable to current algorithms on
automatic localization of facial landmarks. We also show that, in our
experiments, the replacement of texture information by curvature features
produced little change in performance, which is an important finding as it
suggests the applicability of the method to any type of 3D data. |
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More... |
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The above are
highlights of some recent publications. See the full list of publications here |
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