Automatic 3D facial expression recognition
September 16, 2013
Date Performed: September 10, 2013
Instructors: Claudio Esperan¸ca
Facial expressions are an important aspect of human emotion communication.
They indicate the emotional state of a subject, his personality, among other
features. According to Bettadapura , their study begun with clinical and
psychological purposes, but with recent advances on computer vision, computer
science researchers began to show interest on developing systems to automati-
cally detect those expressions.
Automatic facial expression recognition has several applications, as in HCI
(Human-Computer Interaction), where interfaces could be developed in order to
respond to certain user expressions, as in games, communication tools, etc. Al-
though humans can easily recognize a speciﬁc facial expression, its identiﬁcation
by computer systems is not that easy. There are several challenges involved, like
illumination changes, occlusion, use of beards, glasses, etc .
In the 70s, one of the ﬁrst problems faced by researchers was: how to accu-
rately describe an expression? Paul Ekman on his research deﬁned six basic
expressions, which he considered to be universal expressions, because they can
be identiﬁed on any culture. They are: joy, sadness, fear, surprise, disgust and
anger . Examples are shown in Figure 1. Later in 2001, Parrot identiﬁed
136 emotional states and categorized them on three levels: primary, secondary
and tertiary emotions . Primary emotions are Ekman’s six basic emotions,
and the other two levels form a hierarchy. Still in 1971, Ekman wrote a study
claiming facial expressions were universal across diﬀerent cultures .
Figure 1: Universal expressions: joy, sadness, fear, surprise, disgust and anger
In 1977 Ekman and Friesen developed a methodology to measure expressions
in a more precise way, by creating FACS (Facial Action Coding System) .
On FACS, basic expression components are deﬁned, called Action Units (AUs).
They describe small facial movements, such as raising the inner brows (AU1),
or wrinkling the nose (AU9), an so on. These action units can be combined to
form facial expressions.
A discussion about the universality of human expressions arose in 1994, when
Russell questioned Ekman’s position and discussed several points indicating that
human expressions are not universal across diﬀerent cultures . In the same
year, Ekman wrote a paper refuting Russell’s arguments one by one . Since
then Ekman’s position has been widely accepted and the claim that human ex-
pressions are universal across diﬀerent cultures has been sustained.
Facial expression recognition research has many ﬁelds of study. One of them is
3D facial expression recognition. These systems are based on facial surface in-
formation obtained by creating a 3D model of the subject’s face, and they try to
identify the expression in this model. This report will discuss some approaches
used in this ﬁeld. There is a major division between static and dynamic stud-
ies. Static studies are performed on a single picture of a subject, where the
expression is identiﬁed, and dynamic studies consider the temporal behavior
of expressions (see Figure 2). A great example of dynamic studies is micro-
expression analysis. A micro-expression is an expression that happens in a very
short instant of time, generally between 1/25th to 1/15th of a second. They
generally occur when a subject is trying to conceal an expression but fail, and
it will appear for a brief moment on the face.
Figure 2: Example of a dynamic facial expression system
One major problem on facial expression studies is to capture spontaneous ex-
pressions. Most facial expressions databases are composed of simulated expres-
sions, such as the ones displayed in Figure 3. It is easier to ask subjects to
display these expressions than to capture expressions generated spontaneously
based on emotional reactions to real world stimuli. An interesting development
occured when Sebe et al. gave a solution to this problem by using a kiosk with
a camera . People would stop by and watch videos, while displaying genuine
emotions, and their face was being captured by a camera. At the end of the
study, subjects were asked if they would allow their image to be used for aca-
Figure 3: Examples of clearly non-spontaneous facial expressions
2 Facial expression systems
There are many approaches used by facial expression systems. In a recent survey,
Sandbach et al. reviewed the state of the art and they noticed most systems are
organized in three steps: face acquisition, face tracking and alignment, and
expression recognition .
2.1 Face acquisition
Face acquisition is a step performed with the objective of generating a 3D model
of the subject’s face. There are some approaches, such as single image recon-
struction, use of structured light, stereo photometry and multi-vision stereo
Single image reconstruction methods are an emerging research topic, because
of its simplicity: only a single image is required, using an ordinary camera in
a non-restricted environment. Blanz and Vetter developed a method called 3D
Morphable Models (3DMM), which statistically builds a model combining infor-
mation of 3D shape and 2D texture . The method can be used to generate
linear combinations of diﬀerent expressions and use them to synthesize expres-
sions and detect them on facial models. The main disadvantages are: some
initialization is required and the method is not robust to partial occlusions.
Structured light techniques are based on projecting a light pattern on the sub-
ject’s face, analyzing the pattern deformations and recovering 3D shape infor-
mation. Figure 4 shows an example of such systems. Hall and Rusinkiewicz
developed a system using multiple patterns, which are alternately displayed on
the face . An image without the pattern can also be captured in order to
incorporate 2D texture information on the 3D model.
Figure 4: Illustration of a structured light system
Stereo photometry is a variation of structured light techniques which uses more
than one light, and each one can emit a diﬀerent color, as shown in Figure 5.
Such systems can retrieve surface normals, which can be integrated in order
to recover 3D shape information. Jones et al. developed a system which uses
three lights switching on and oﬀ in a cycle around the camera . The system
performs well using either visible or infrared light.
Figure 5: Illustration of a stereo photometry system
Multi-vision stereo acquisition systems use more than one camera to simul-
taneously capture images from diﬀerent angles and combine these images to
reconstruct the scene. Beeler at al. developed a system which uses high-end
cameras and standard illumination, showing great results with sub-millimeter
2.2 Face tracking and alignment
The second step performed on most facial expression systems is face tracking
and alignment. Given two meshes, the problem is to align them in 3D space,
so that they can be tracked over time. There are two kinds of alignment: rigid
approaches, which assume similar meshes without large transformation, and
non-rigid, which deals with large transformations. Most rigid-based approaches
rely on the traditional ICP (Iterative Closest Point) algorithm . As for non-
rigid approaches, there are several diﬀerent ways to perform the alignment.
Amberg et al. created a variant of ICP which adds a stiﬀness variable to con-
trol the rigidity of the transformation at each iteration . The stiﬀness value
starts with a high value and is reduced at each iteration, so that the matching
will gradually allow a non-rigid transformation to be performed. Rueckert et
al. used a FFD (Free-Form Deformation) model which performs deformations
using control points . By reducing the number of points, computing time
can be reduced as well. See Figure 6 for an example of a FFD model.
Figure 6: Free-Form Deformation model
Wang et al. used harmonic maps to perform the alignment . The face is
mapped from 3D space to 2D space, by projecting the mesh into a disc, as shown
in Figure 7, thus reducing one dimension. Diﬀerent discs can be compared in
order to perform alignment. Sun et al. used a similar technique called conformal
mapping, which maps the mesh into a 2D space, preserving the angles between
edges . Tsalakanidou and Malassiotis modiﬁed ASMs (Active Shape Models)
 to work in 3D, using a face model with the most prominent features, such
as eyes, nose, etc . Figure 8 shows examples of ASMs plotted on the faces.
2.3 Expression recognition
The third and last step of a facial expression system is to recognize the expres-
sion. In this step, descriptors are extracted, selected and classiﬁed using artiﬁcial
intelligence techniques. Features can be static or dynamic. Static features are
mostly used on a single image, whereas dynamic features have the property to
be stable across time, and can be tracked through successive frames on a video
Figure 7: Harmonic maps
Figure 8: Active Shape Models
analysis. Temporal modeling can be done in order to analyze the dynamics of
the expression through time. Most systems use HMMs (Hidden Markov Models)
 to perform this task. Common static features are distance-based, patch-
based, morphable models and 2D representations.
Distance-based features rely on distances between facial attributes, such as the
distance between the corners of the mouth, or between the mouth and the eye,
and so on. Soyel and Demirel used 3D distances to recognize expressions .
Maalej et al. used patch-based features, where patches are small regions of the
mesh represented as surface curves , as shown in Figure 9. Patches are
compared against templates by computing the geodesic distance between them.
Ramanathan et al. used a MEM (Morphable Expression Model), where base
expressions are deﬁned and any expression could be modeled through a linear
combination between these base expressions by using morphing parameters .
These parameters deﬁne a parameter space, where similar expressions form clus-
ters. A new expression is identiﬁed by ﬁnding the parameters which generate
the closest expression and passing these parameters to a classiﬁer. Berretti et al.
used 2D representations, where the depth map of the face is computed, generat-
ing a 2D image . Classiﬁcation is done using SIFT (Scale Invariant Feature
Transform) descriptors  and SVMs (Support Vector Machines) .
Figure 9: Patch-based descriptors
As for dynamic features, there are a few approaches. Le et al. used facial level
curves, since their variation through time can be tracked and calculated using
Chamfer distances . Figure 10 shows an example of such curves. Sandbach
et al. used FFDs to model the lattice deformation over time, and they used
HMMs to perform temporal analysis .
Figure 10: Facial level curves
Feature classiﬁcation is generally performed using well known classiﬁers, such
as AdaBoost and variations , k-NNs (k-Nearest Neighbors) , Neural Net-
works , SVMs , etc.
3 Future challenges
Research on 3D facial expression recognition is evolving, but there are some
challenges to consider. One is the construction of more spontaneous expressions
databases, since most of them were built using artiﬁcial expressions. Further-
more, the development of systems capable to distinguish a spontaneous expres-
sion from an artiﬁcial one is also desirable. Recognition of expressions other
than Ekman’s six universal expressions is important, since most systems focus
only on these six. Temporal analysis is still on its infant stage. More focus on
this area is required, especially on the analysis of micro-expressions, which are
very hard to detect. Improvement on algorithms performance is also a crucial
factor. Ideally, all systems should work in real-time.
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