Tracking of facial features to support human-robot interaction

Abstract

In this paper we present a novel methodology for detection and tracking of facial features like eyes, nose and mouth in image sequences. The proposed methodology is intended to support natural interaction with autonomously navigating robots that guide visitors in museums and exhibition centers and, more specifically, to provide input for the analysis of facial expressions that humans utilize while engaged in various conversational states. For face and facial feature region detection and tracking, we propose a methodology that combines appearance-based and feature-based methods for recognition and tracking, respectively. For the stage of face tracking the introduced method is based on least squares matching (LSM), a matching technique able to model effectively radiometric and geometric differences between image patches in different images. Thus, compared with previous research, the LSM approach can overcome the problems of variable scene illumination and head in-plane rotation. Another significant characteristic of the proposed approach is that tracking is performed on the image plane only wherever laser range information suggests so. The increased computational efficiency meets the real time demands of human-robot interaction applications and hence facilitates the development of relevant systems.

Full text

Tracking of Facial Features to Support Human-Robot Interaction
Maria Pateraki, Haris Baltzakis, Polychronis Kondaxakis, Panos Trahanias
Institute of Computer Science,
Foundation for Research and Technology - Hellas,
Heraklion, Crete, Greece
{pateraki,xmpalt,konda,trahania}@ics.forth.gr
Abstract— In this paper we present a novel methodology
for detection and tracking of facial features like eyes, nose
and mouth in image sequences. The proposed methodology
is intended to support natural interaction with autonomously
navigating robots that guide visitors in museums and exhi-
bition centers and, more specifically, to provide input for the
analysis of facial expressions that humans utilize while engaged
in various conversational states. For face and facial feature
region detection and tracking, we propose a methodology that
combines appearance-based and feature-based methods for
recognition and tracking, respectively. For the stage of face
tracking the introduced method is based on Least Squares
Matching (LSM), a matching technique able to model effectively
radiometric and geometric differences between image patches
in different images. Thus, compared with previous research, the
LSM approach can overcome the problems of variable scene
illumination and head in-plane rotation. Another significant
characteristic of the proposed approach is that tracking is
performed on the image plane only wherever laser range
information suggests so. The increased computational efficiency
meets the real time demands of human-robot interaction
applications and hence facilitates the development of relevant
systems.
I. INTRODUCTION
A key enabling technology for next-generation robots for
the service, domestic and entertainment market is Human-
Robot-Interaction. A socially interactive robot, i.e a robot
that collaborates with humans on a daily basis (be this
in care applications, in a professional or private context)
requires interactive skills that go beyond keyboards, button
clicks or metallic voices. For this class of robots, human-like
interactivity is a fundamental part of their functionality. Some
of the greatest challenges towards this goal are related to how
robots perceive the world. As pointed out in [1], in order
to interact meaningfully with humans, a socially interactive
robot must be able to perceive, analyze and interpret the
state of the surrounding environment and/or humans in a
way similar to the way humans do. In other words, it must be
able to sense and interpret the same phenomena that humans
observe.
Unlike humans that mostly depend on their eyes, most
current robots, in addition to vision sensors, also utilize range
sensors like sonars, infrared sensors and laser range scanners.
Approaches based on range sensors are very popular for tasks
This work has been partially supported by the EU Information Society
Technologies research project INDIGO (FP6-045388). and the Greek na-
tional GSRT project XENIOS.
like autonomous navigation [2], [3], [4], [5] and 2D people
tracking [6]. The main advantage of such sensors over vision
ones is that they are capable of providing accurate range
measurements of the environment in large angular fields and
at very fast rates. On the other hand, for tasks like gesture
recognition and face detection, i.e tasks that require richer
information (e.g intensity, color) or information beyond the
2D scanning plane of a typical range sensor setup, vision is
the only alternative [7].
In this paper we present a novel methodology for detection
and tracking of facial features like eyes, nose and mouth
in image sequences. The proposed approach is intended to
support natural interaction with autonomously navigating
robots that guide visitors in museums and exhibition centers
and, more specifically, to provide input for the analysis
of facial expressions that humans utilize while engaged in
various conversational states. The operational requirements
of such an application challenge existing approaches in that
the visual perception system should operate efficiently un-
der unconstrained conditions regarding occlusions, variable
illumination, moving cameras, and varying background. The
proposed approach combines and extends multiple state-of-
the art techniques to solve a number of related subproblems
like (a) detection and tracking of people in both the ground
plane and the image plane, (b) detection and tracking of
human faces on the image plane and, (c) tracking of specific
facial features like eyes, nose and mouth on the image plane.
People tracking, given the constrains of the application at
hand, is a very challenging task by itself. This is because the
applied method must be computationally efficient, in order
to perform in almost real-time, and robust in the presence
of occlusions, variable illumination, moving cameras and
varying background. A thorough survey on vision-based
approaches to people tracking, can be found in [7]. The
referenced methods rely to a great extent on visual detection
of head or face and tend to be time-consuming and less robust
in uncontrolled lighting conditions. Laser-based detection
and tracking can provide a more reliable automatic detection
of humans in dynamic scenes, using one [8], [9], [10] or
multiple registered laser scanners [11]. However, the lack of
color information, causes difficulties in laser- based methods,
e.g. to maintain tracked trajectories of different objects when
occlusions occur. Therefore, the combination of distance and
angle information, obtained from a laser scanner with visual
information, obtained from a camera, could support vision-
2009 IEEE International Conference on Robotics and Automation
Kobe International Conference Center
Kobe, Japan, May 12-17, 2009
978-1-4244-2789-5/09/$25.00 ©2009 IEEE 3755

based methods for faster and more reliable human tracking.
In the field of robotics, ”hybrid” methods combining laser
and camera data have appeared recently, and in [12] repre-
sentative methods, e.g. [13], [14], are discussed.
Tracking of human faces and facial features on the image
plane constitutes another challenging task because of face
variability in location, scale, orientation (up-right, rotated),
pose (frontal, profile), age and expression. Furthermore,
it should be irrespective of lighting conditions and scene
content. Detection can be based on different cues: skin color
(color images/videos), motion (videos), face/head shape,
facial appearance, or a combination of them. Comprehen-
sive surveys on face detection and tracking are [15], [16].
Appearance- based methods avoid difficulties in modeling
3D structures of faces by considering possible face ap-
pearances under various conditions with AdaBoost learning-
based algorithms, e.g. [17], [18], [19], to be the most
effective so far. Color-based systems may be computationally
attractive but the color constraint alone is insufficient for
achieving high accuracy face detection, mainly due to large
facial color variation in different lighting conditions and
humans of different skin color. Other methods primarily
based on color models, e.g. [20] may prove more robust
in laboratory environments but in unconstrained lighting and
illumination still their performance is limited and are less
suitable to derive head rotations.
The most important contribution of this paper is related to
the methodology used for for face and facial feature region
detection and tracking which combines appearance-based
and feature-based methods for recognition and tracking,
respectively. For the stage of face tracking the introduced
method is based on Least Squares Matching (LSM), a
matching technique able to model effectively radiometric and
geometric differences between image patches in different im-
ages. Compared with previous research, the LSM approach
can overcome the problems of variable scene illumination
and head in- and off-plane rotations.
Another significant characteristic of the proposed method-
ology is that visual people tracking is performed only
wherever laser range information suggests so. The increased
computational efficiency meets the real time demands of the
specific application at hand and facilitates its application to
other crucial robotics tasks as well. Moreover, since informa-
tion encapsulated in visual data acts supplementary to laser
range information, inherent advantages of both sensors are
maintained, leading to implementations combining accuracy,
efficiency and robustness at the same time.
The proposed methodology was tested extensively with
a variety of real data gathered with two different robotic
platforms, both in laboratory and in real museum/exhibition
setups. The results obtained are very promising and demon-
strate its effectiveness.
II. METHODOLOGY OVE RVIEW
The basic idea in the proposed methodology is to suf-
ficiently exploit the detection capability of laser scanners
and to combine visual information (both grey-level and color
Fig. 1. Overview of laser/vision based system
images can be used) to the detection results to track people,
as well as face and facial features in dynamic scenes. The
temporal detection of humans relies on laser-based detection
and tracking of moving objects (DATMO), i.e. of moving
legs, and registration of vision-based information to localize
the expected face region. After human detection, the face
and facial features are detected and tracked over time. The
tracking information of facial features is used for a later
analysis of conversational states.
The methodology is schematically depicted in Fig. 1,
where it can be seen that once moving objects have been
detected using the Laser Range Scanner (LRS) data, humans
are tracked by integrating calibrated camera information and
field of view, distance from laser scanner (baseline) and
minimum and maximum man height. The face that is frontal
and closest to the camera is detected and its position and
rotation are tracked. Within the enclosing area of the detected
face, the facial subregions, i.e. eyes, mouth and nose, are then
detected by imposing anthropometric constraints. Tracking of
these features is done by area-based matching approaches.
In both the tracking of face and facial regions quality
measures of the matching result are given and evaluated.
These measures will determine if the final tracked result
should be accepted or not.
III. LAS ER-BA SED DE TEC TIO N AND TRACKING OF
MOVI NG TARG ETS
To extract and track multiple moving targets (e.g. legs)
from stationary background (e.g. building walls, desks,
chairs, etc.) a Joint Probabilistic Data Association with
Interacting Multiple Model (JPDA-IMM) algorithm is em-
ployed. Its robustness, in comparison to other techniques, is
thoroughly presented in [6] and here the consecutive steps are
briefly described. Initially, a single occupancy grid is created
and every individual grid-cell accumulates a counter, which
increases linearly when a laser measurement falls inside its
occupancy area. Grid-cells with values above a dynamic
threshold level are selected as stationary background. A
certain background is obtained and subtracted from every
laser frame leaving only the measurements that represent
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possible moving targets. The remaining measurements are
clustered into groups and a center of gravity is assigned
to each one. Finally, the JPDA-IMM initiates and allocates
tracks to clusters that exceed a certain velocity level.
The technique effectively distinguishes targets which move
at close proximity to each other, being also able to com-
pensate the relative movement of the robot. The identified
moving targets are treated as potential leg candidates, con-
sidering also that, apart from humans, other moving objects
may appear in the scene.
IV. VISION-BA SED SYSTEM
The camera is adjusted on the same robotic platform as
the LRS, at an appropriate height to view humans face on,
and its optical axis parallel to the LRS pointing direction.
The camera is calibrated using Zhang’s method [21], and
calibration information is utilized at a later stage for LRS
and vision data registration.
A. Human detection
Camera calibration parameters, image resolution, frame
rate, known baseline between the LRS and the video camera
are used to register the image with the LRS data. The
laser points, indicated as leg candidates, are projected on
the visual image plane and by including information on the
minimum and maximum human height, e.g. 1.20 m and
1.90 m, respectively, the expected face region is localized in
the image plane. The results of the method are demonstrated
in Fig. 2(a), where the ellipses mark the four moving objects,
corresponding to four legs, and in Fig. 2(b), where the
expected face region is localized using the registered LRS
and vision data and the human height constraints.
B. Face Detection and Tracking
Following body region localization the faces are detected
within the given region in order to reject possible outliers
arising after LRS processing and verify that there are people
moving towards the robot. We assume that for the initial
detection of the person interacting with the robot we have
frontal views of the person. However, we still have to tackle
the issues of in- and off-plane head rotations in face track-
ing, important in the analysis of communicative signs and
variable scene illumination. With respect to illumination, our
(a) (b)
Fig. 2. Registered LRS and vision data. (a) The detected moving objects
are marked within the ellipses, (b) the localized expected face areas
Fig. 3. Detection of faces
aim is to place the robot in environments with unconstrained
lighting conditions.
We utilize a hybrid approach by integrating an appearance-
based approach for face detection and a feature-based ap-
proach for face tracking. In the introductory part the ad-
vantages of appearance-based methods have been already
pointed out. The robust face detector developed by Viola and
Jones [19] is employed in this work. The named detector
combines four key concepts; Haar features, integral image
for rapid feature detection, the Adaboost machine-learning
method and a cascaded classifier to combine many features
efficiently. Unfortuantelly, this approach suffers from two
significant limitations: (a) inability to handle significant in-
plane rotations (i.e. rotations of 30 degrees or more), and (b)
increased processing time. Although some recent approaches
(e.g. [18], [22]) tackle with the first limitation (inability to
track in-plane rotations) using an extended set of rotated
Haar-like features, the required computational power still
prohibits their use to applications that involve higher fram-
erates and/or higher image resolutions. Moreover, they aim
in detecting faces in every input frame without maintaining
ids of specific persons, i.e they do not perform face tracking.
Therefore, in our method the face is detected in the
initial frame using the Haar method and tracked in the
subsequent frames with the LSM approach, described in the
next paragraph. Only if the LSM tracker fails to converge to
a solution, the Haar detector will re-initialize the process. In
Fig. 3 the rectangles mark the detected faces on the image
by the Haar method.
C. A Least-Squares Approach to Face Tracking
Cross-correlation is based on the assumption that geo-
metric differences are modeled only by translation, and
radiometric differences exist only due to brightness and
contrast. Thus, its precision is limited, decreases rapidly if
the geometric model is violated (rotations greater than 20◦
and scale differences between images greater than 30%). A
generalization of cross correlation is Least Squares Matching
(LSM) [23], which in its general approach can compensate
geometric differences in rotation, scale and shearing.
Several approaches exist in the current literature, mainly
from the photogrammetric community, using least squares
for image registration, calibration, surface reconstruction etc.
To the best of our knowledge, the iplementation of LSM
described in this paper, is introduced for the first time face
tracking in a robotic application.
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The formulation of the general estimation model is based
on the assumption that there are two or more image win-
dows (called image patches) given as discrete functions
f(x,y),gi(x,y),i=1, ...n−1, wherein fis the template
and gthe search image patch in i=1, ...n−1 search
images. The problem statement is finding the corresponding
part of the template image patch f(x,y)in the search images
gi(x,y),i=1, ...n−1.
f(x,y)−ei(x,y) = gi(x,y)(1)
Equation (1) gives the least squares grey level observa-
tion equations, which relate the f(x,y)and gi(x,y)image
functions or image patches. The true error vector ei(x,y)
is included to model errors that arise from radiometric
and geometric differences in the images. For the selection
of the geometrical model it is assumed that the object
surface is approximated by local planar facets and an affine
transformation is generally used. Radiometric corrections
(e.g. equalization), for compensation of different lighting
conditions are not included in the model but are applied
during LSM.
In our implementation we use two images and the affine
transformation is applied with respect to an initial position
x0,y0:
x=a0+a1·x0+a2·y0
y=b0+b1·x0+b2·y0
(2)
After linearization of the function g(x,y), (1) becomes:
f(x,y)−e(x,y) = g(x0,y0) + ∂g(x0,y0)
∂x·dx +∂g(x0,y0)
∂y·dy
(3)
With the simplified notation:
gx=∂g(x0,y0)
∂x,gy=∂g(x0,y0)
∂y
and by differentiating (2), then (3) results in:
f(x,y)−e(x,y) = g(x0,y0) + gxda0+gxx0da1+gxy0da2+
+gydb0+gyx0db1+gyy0db2
(4)
with the parameter vector xbeing defined as:
xT= (da0,da1,da2,db0,db1,db2)(5)
The least squares solution of the system is given by (6):
ˆx= (ATPA)−1(ATPl)(6)
where ˆxis the vector of unknowns, Ais the design matrix of
grey level observation equations, Pis the weight matrix, and
lis the discrepancy vector of the observations. The weights
are typically diagonal with elements set to unity for all the
grey level observation equations. The number of grey level
observations is related to the size of the template size. If e.g.
a patch size of 9x9 is selected then the number of observation
equations is 81.
In our implementation of LSM for face tracking, the affine
is constrained to conformal transformation to avoid over-
parametrization of the system since only estimation of shifts,
rotation and scale suffice to model the geometric differences
of frontal faces in frame sequence. The geometric differences
refer to: (a) face scaling, when the person moves closer or
away from the robot, (b) head in-plane rotations and (c) head
off-plane rotations. It is known that if there is insufficient
signal content or if the initial approximations in the least
squares solution are not close to the real solution, the solution
will not converge. These issues can be easily handled in
face tracking. Initial approximations for shifts are taken from
the center of the face area detected with the Haar method
that initialized the process of face localization. The template
used for temporal matching, is initialized to the center of the
detected face and scaled at 75% of its area. Equalization is
applied both for the template and the search area during LSM
to compensate for radiometric differences. The template is
updated when solution converges and the process continues.
Variable illumination poses less an issue since the search area
expands around the initial patch at a maximum of half the
size of the largest dimension of the patch. The only drawback
is the size of the template when the face is very close to
the camera, increasing the number of observation equations.
A proposed solution is to apply LSM in images of lower
resolution and transform the matching results to the original
level.
As far as quality is concerned, the criteria used to evaluate
matching results are the number of iterations, the alteration
of the size of parameters in each iteration and the size
of parameters. The number of iterations is a rather good
criterion, assuming that the approximations are good. In
parallel, variations in the parameter values (magnitude and
sign) in each iteration have to be observed in order to
evaluate the stability of the solution. The threshold for the
iterations should not be set too high (maximum number of
15 iterations), considering that fast convergence should be
achieved, since the initial values are close to the correct
solution. The size of parameters, especially the estimated
value for scales, should not exceed an upper (>3.0) and
lower value (<0.3) and the difference to their initial values
should be small. The initial values for scales are set to 1.
The variation of x, y coordinates from their initial values is
also checked, considering the utilized frame rate and if it is
above a certain threshold the point is rejected.
D. Facial Feature Region Detection
Eyes, nose and mouth are the facial regions that are
detected. However, the image resolution of the face is an
important factor in facial feature region detection. When the
face area is smaller than 70 x 90 pixels the facial regions
become hard to detect [24]. As in the case of face detection,
individual sets of Haar-like features for each region are used
to detect the eyes, mouth and nose area, within the detected
and tracked face region using the method from [19].
False detections may arise especially in faces of larger
scale or higher resolution and anthropometric constraints are
imposed for reliability of the solution. The eye, mouth and
nose regions should be found in the upper half, lower half
and in the central part of the face respectively, while the ratio
of their respective widths to the width of the face by coarse
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(a) (b) (c)
(d) (e) (f)
Fig. 4. Examples of face and facial features tracking results under variable lighting conditions and background
approximation should be close to 0.4, empirically learned
from normalized measurements in a number of frontal faces.
These constraints proved to enforce detection.
E. Facial Feature Region Tracking
The detected facial feature regions are tracked in the
subsequent video frames by using cross-correlation of image
patches. This approach is justified as there are only small
deviations in the relative positions of these feature areas with
respect to the position of the detected and tracked face within
the image. The previously detected eye, nose and mouth
regions are used as templates in the matching process. The
process is computationally efficient, since Haar detection is
only used for the initial detection, whereas the templates are
taken from the same object, used in the tracking procedure.
V. EXP ERI MEN TAL RE SULTS
The method proposed in this paper has been implemented
and assessed on two different robotic platforms in various
laboratory and real application setups.
For laboratory testing, we utilized an iRobot-B21r plat-
form equipped with a SICK-PLS laser range finder and
a low-end camera operating at a standard resolution of
640×480 pixels. The range finder is capable of scanning 180
degrees of the environment, with an angular resolution of one
measurement per degree and a range measuring accuracy of
5cm.
The platform that was utilized to collect data from the
actual exhibition place, was a Neobotix NE-470 robotic plat-
form equipped with a PointGray Bumblebee2 stereovision
head, operating at the same, 640 ×480 resolution and a Sick
S300 laser range finder. This specific range finder is capable
of achieving an angular resolution of two measurements per
degree and its placement in front of the robot ensured a field
of view of about 210 degrees.
Prior to testing our methodology, an internal calibration
procedure has been applied to both robots in order to estimate
the relative positions and the intrinsic parameters of all
sensors.
Examples from the human tracking results have been
previously shown in Figs. 2 and 3.
The proposed methodology for face and facial feature
detection and tracking was tested with the different video
sequences recorded in the laboratory, as well as in various
locations at the exhibition place. Fig. 4 shows results from
the video sequences, recorded at various locations of the
exhibition place. As it can be seen the method is able to
handle severe illumination and background conditions, yet
extract face and facial features reliably. Images in Fig. 4(a)
and 4(b), were recorded in a room with low lighting and
monitors in the background, that pose a problem for methods
employing background subtraction. Even in the case of
strong background lightning, as in Fig. 4(b), feature areas
can be tracked. Moreover, our method is able to handle (a)
dynamic backgrounds, e.g. Figs. 4(d), 4(e), 4(f), (b) scene
objects being very close in color to skin, e.g. Figs. 4(b) and
4(f) (clothes in similar color to skin).
In addition the comparative advantage of the LSM method
for face tracking over other commonly used methods is
demonstrated. LSM is able to compensate geometric and
radiometric differences between image patches. In Fig. 5,
results of LSM versus pure Haar-based face tracking and the
CMU tracker are shown. The indicative images are selected
from a video sequence recorded in the laboratory. As can be
easily observed, in this result, the Haar-based method fails
to track the face when in-plane rotations occur. The CMU
method also fails to provide reliable results for position as
well as for rotation, whereas the LSM provides a solution,
along with certain measures to evaluate the tracking result.
In all experiments conducted, including the ones presented
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above, the LSM tracking operated at a frame rate of 30 fps,
on a Pentium Core Duo 2.8 GHz.
VI. CONCLUSIONS AND FUTURE WORK
In this paper we have presented a novel methodology
for robust detection and tracking of human faces and fa-
cial features in image sequences, intended for human-robot
interaction applications.
According to the proposed methodology, the 3D locations
of people, as produced by a people tracker that utilizes laser
range data to track people on the ground plane, are projected
on the image plane in order to identify image regions that
may contain human faces. A state-of-the-art, appearance-
based method is used in order to specifically detect human
faces within these regions and initiate a feature based tracker
that tracks these faces as well as specific facial features over
time.
Experimental results have confirmed the effectiveness
and the increased computational efficiency of the proposed
methodology, proving that the individual advantages of all
involved components are maintained, leading to implemen-
tations that combine accuracy, efficiency and robustness at
the same time.
We intend to use the proposed methodology in order
to support natural interaction with autonomously navigating
robots that guide visitors in museums and exhibition centers.
More specifically the proposed methodology will provide
input for the analysis of facial expressions that human utilize
while engaged in various conversational states.
Future work includes extension of the LSM temporal
tracking to handle stereo vision by exploiting epipolar con-
straints. Moreover, the methodology presented in the paper
will be employed in an integrated system for naturalistic
human-robot interaction.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Fig. 5. Comparison of Haar, CMU and LSM face tracker in the presence
of in-plane rotation. Results from the Haar-based detection in (a), (b), (c),
the CMU tracker in (d), (e), (f) and the LSM tracker in (g), (h), (i).
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