COST2102_Chapter_Bartlett.doc

Applying automated facial expression coding to
datamine spontaneous human behavior

Marian Bartlett1, Gwen Littlewort1, Javier Movellan1, Esra Vural13,
Kang Lee2, Mujdat Cetin3 , and Aytul Ercil3
1
Institute for Neural Computation, University of California, San Diego, La Jolla, CA
92093-0445, USA
2
Human Development and Applied Psychology, University of Toronto, Ontario, Canada
3
Engineering and Natural Science, Sabanci University, Istanbul, Turkey
mbartlett@ucsd.edu; gwen@mpmlab.ucsd.edu, movellan@mplab.ucsd.edu,
vesra@ucsd.edu, kang.lee@utoronto.ca

Abstract. The computer vision field has advanced to the point that we are now
able to begin to apply automatic facial expression recognition systems to
important research questions in behavioral science. The machine perception lab
at UC San Diego has developed a system based on machine learning for fully
automated detection of 30 actions from the facial action coding system (FACS).
The system, called Computer Expression Recognition Toolbox (CERT),
operates in real-time and is robust to the video conditions in real applications.
This paper describes two experiments which are the first applications of this
system to analyzing spontaneous human behavior: Automated discrimination of
posed from genuine expressions of pain, and automated detection of driver
drowsiness. Issues for application of machine learning systems to facial
expression analysis are discussed.

Keywords: Facial expression recognition, machine learning.

1 Introduction

The computer vision field has advanced to the point that we are now able to begin
to apply automatic facial expression recognition systems to important research
questions in behavioral science. This paper is among the first applications of fully
automated facial expression measurement to such research questions. It explores two
applications of a machine learning system for automatic facial expression
measurement to datamine spontaneous human behavior (1) differentiating fake from
real expressions of pain, and (2) detecting driver drowsiness.

1.1 The Facial Action Coding System

The facial action coding system (FACS) (Ekman and Friesen, 1978) is arguably the
most widely used method for coding facial expressions in the behavioral sciences.
The system describes facial expressions in terms of 46 component movements, which

roughly correspond to the individual facial muscle movements. An example is shown
in Figure 1. FACS provides an objective and comprehensive way to analyze
expressions into elementary components, analagous to decomposition of speech into
phonemes. Because it is comprehensive, FACS has proven useful for discovering
facial movements that are indicative of cognitive and affective states. See Ekman and
Rosenberg (2005) for a review of facial expression studies using FACS. The primary
limitation to the widespread use of FACS is the time required to code. FACS was
developed for coding by hand, using human experts. It takes over 100 hours of
training to become proficient in FACS, and it takes approximately 2 hours for human
experts to code each minute of video. The authors have been developing methods for
fully automating the facial action coding system (e.g. Donato et al., 1999; Bartlett et
al., 2006). In this paper we apply a computer vision system trained to automatically
detect FACS to datamine facial behavior under two conditions: (1) real versus fake
pain, and (2) driver fatigue.

Figure 1. Example facial action decomposition from the facial action coding
system. A prototypical expression of fear is decomposed into 7 component
movements. Letters indicate intensity. A fear brow (1+2+4) is illustrated here.

1.2 Spontaneous Expressions

The machine learning system presented here was trained on spontaneous facial
expressions. The importance of using spontaneous behavior for developing and
testing computer vision systems becomes apparent when we examine the neurological
substrate for facial expression. There are two distinct neural pathways that mediate
facial expressions, each one originating in a different area of the brain. Volitional
facial movements originate in the cortical motor strip, whereas spontaneous facial
expressions originate in the subcortical areas of the brain (see Rinn, 1984, for a
review). These two pathways have different patterns of innervation on the face, with
the cortical system tending to give stronger innervation to certain muscles primarily
in the lower face, while the subcortical system tends to more strongly innervate
certain muscles primarily in the upper face (e.g. Morecraft et al., 2001).
The facial expressions mediated by these two pathways have differences both
in which facial muscles are moved and in their dynamics (Ekman, 2001; Ekman
& Rosenberg, 2005). Subcortically initiated facial expressions (the spontaneous

group) are characterized by synchronized, smooth, symmetrical, consistent, and
reflex-like facial muscle movements whereas cortically initiated facial expressions
(posed expressions) are subject to volitional real-time control and tend to be less
smooth, with more variable dynamics (Rinn, 1984; Frank, Ekman, & Friesen, 1993;
Schmidt, Cohn & Tian, 2003; Cohn & Schmidt, 2004). Given the two different
neural pathways for facial expressions, it is reasonable to expect to find differences
between genuine and posed expressions of states such as pain or drowsiness.
Moreover, it is crucial that the computer vision model for detecting states such as
genuine pain or driver drowsiness is based on machine learning of expression samples
when the subject is actually experiencing the state in question.

2 The Computer Expression Recognition Toolbox (CERT)

Here we extend a system for fully automated facial action coding developed
previously by the authors (Bartlett et. Al, 2006; Littlewort et al., 2006). It is a user
independent fully automatic system for real time recognition of facial actions from the
Facial Action Coding System (FACS). The system automatically detects frontal faces
in the video stream and codes each frame with respect to 20 Action units. In previous
work, we conducted empirical investigations of machine learning methods applied to
the related problem of classifying expressions of basic emotions. We compared image
features (e.g. Donato et al., 1999), classifiers such as AdaBoost, support vector
machines, and linear discriminant analysis, as well as feature selection techniques
(Littlewort et al., 2006). Best results were obtained by selecting a subset of Gabor
filters using AdaBoost and then training Support Vector Machines on the outputs of
the filters selected by AdaBoost. An overview of the system is shown in Figure 3.

Figure 3. Overview of the automated facial action recognition system.

2.1 Real Time Face and Feature Detection

We employed a real-time face detection system that uses boosting techniques in a
generative framework (Fasel et al.) and extends work by Viola and Jones (2001).
Enhancements to Viola and Jones include employing Gentleboost instead of

AdaBoost, smart feature search, and a novel cascade training procedure, combined in
a generative framework. Source code for the face detector is freely available at http://
kolmogorov.sourceforge.net. Accuracy on the CMU-MIT dataset, a standard public
data set for benchmarking frontal face detection systems (Schneiderman & Kanade,
1998), is 90% detections and 1/million false alarms, which is state-of-the-art
accuracy. The CMU test set has unconstrained lighting and background. With
controlled lighting and background, such as the facial expression data employed here,
detection accuracy is much higher. All faces in the training datasets, for example,
were successfully detected. The system presently operates at 24 frames/second on a 3
GHz Pentium IV for 320x240 images. The automatically located faces were rescaled
to 96x96 pixels. The typical distance between the centers of the eyes was roughly 48
pixels. Automatic eye detection (Fasel et al., 2005) was employed to align the eyes in
each image. The images were then passed through a bank of Gabor filters 8
orientations and 9 spatial frequencies (2:32 pixels per cycle at 1/2 octave steps).
Output magnitudes were then passed to the action unit classifiers.

2.2 Automated Facial Action Classification

The training data for the facial action classifiers came from three posed datasets and
one dataset of spontaneous expressions. The facial expressions in each dataset were
FACS coded by certified FACS coders. The first posed datasets was the Cohn-Kanade
DFAT-504 dataset (Kanade, Cohn & Tian, 2000). This dataset consists of 100
university students who were instructed by an experimenter to perform a series of 23
facial displays, including expressions of seven basic emotions. The second posed
dataset consisted of directed facial actions from 24 subjects collected by Ekman and
Hager. Subjects were instructed by a FACS expert on the display of individual facial
actions and action combinations, and they practiced with a mirror. The resulting video
was verified for AU content by two certified FACS coders. The third posed dataset
consisted of a subset of 50 videos from 20 subjects from the MMI database (Pantic et
al., 2005). The spontaneous expression dataset consisted of the FACS-101 dataset
collected by Mark Frank (Bartlett et. Al. 2006). 33 subjects underwent an interview
about political opinions on which they felt strongly. Two minutes of each subject
were FACS coded. The total training set consisted of 5500 examples, 2500 from
posed databases and 3000 from the spontaneous set.
Twenty linear Support Vector Machines were trained for each of 20 facial actions.
Separate binary classifiers, one for each action, were trained to detect the presence of
the action in a one versus all manner. Positive examples consisted of the apex frame
for the target AU. Negative examples consisted of all apex frames that did not contain
the target AU plus neutral images obtained from the first frame of each sequence.
Eighteen of the detectors were for individual action units, and two of the detectors
were for specific brow region combinations: fear brow (1+2+4) and distress brow (1
alone or 1+4). All other detectors were trained to detect the presence of the target
action regardless of co-occurring actions. A list is shown in Table 1. Thirteen
additional AU’s were trained for the Driver Fatigue Study. Thease are shown in Table
1B

Table 1A. AU detection performance on posed and spontaneous facial actions.
Values are Area under the roc (A’) for generalization to novel subjects.

AU Name Posed Spont
1 Inner brow raise .90 .88
2 Outer brow raise .94 .81
4 Brow Lower .98 .73
5 Upper Lid Raise .98 .80
6 Cheek Raise .85 .89
7 Lids tight .96 .77
9 Nose wrinkle .99 .88
10 Upper lip raise .98 .78
12 Lip corner pull .97 .92
14 Dimpler .90 .77
15 Lip corner Depress .80 .83
17 Chin Raise .92 .80
18 Lip Pucker .87 .70
20 Lip stretch .98 .60
23 Lip tighten .89 .63
24 Lip press .84 .80
25 Lips part .98 .71
26 Jaw drop .98 .71
1,1+4 Distress brow .94 .70
1+2+4 Fear brow .95 .63
Mean: .93 .77

Table 1B: Additional 13 AU’s trained for the driver fatigue study.

AU Name
8 Lip Toward Each Other
11 Nasolabial Furrow Deepener
13 Sharp Lip Puller
16 Lower Lip Depress
19 Tongue Show
22 Lip Funneller
27 Mouth Stretch
28 Lips Suck
30 Jaw Sideways
32 Bite
38 Nostril Dilate
39 Nostril Compress
45 Blink

The output of the system was a real valued number indicating the distance to the
separating hyperplane for each classifier. Previous work showed that the distance to
the separating hyperplane (the margin) contained information about action unit

intensity (e.g. Bartlett et al., 2006).
In this paper, area under the ROC (A’) is used to assess performance rather than
overall percent correct, since percent correct can be an unreliable measure of
performance, as it depends on the proportion of targets to non-targets, and also on the
decision threshold. Similarly, other statistics such as true positive and false positive
rates depend on decision threshold, which can complicate comparisons across
systems. A’ is a measure is derived from signal detection theory and characterizes the
discriminative capacity of the signal, independent of decision threshold. The ROC
curve is obtained by plotting true positives against false positives as the decision
threshold shifts from 0 to 100% detections. The area under the ROC (A’) ranges from
0.5 (chance) to 1 (perfect discrimination). A’ can also be interpreted in terms of
percent correct. A’ is equivalent to the theoretical maximum percent correct
achievable with the information provided by the system when using a 2-Alternative
Forced Choice testing paradigm.
Table 1 shows performance for detecting facial actions in posed and spontaneous
facial actions. Generalization to novel subjects was tested using 3-fold cross-
validation on the images in the training set. Performance was separated into the
posed set, which was 2,500 images, and a spontaneous set, which was 1100 images
from the FACS-101 database which includes speech.

3 Classification of Real versus Faked Pain Expressions

An important issue in medicine is the ability to distinguish real pain from faked
pain, (malingering). Some studies suggest that malingering rates are as high as 10%
in chronic pain patients (Fishbain et a., 1999), and much higher in litigation contexts
(Schmand et al., 1998). Even more important is to recognize when patients are
experiencing genuine pain so that their pain is taken seriously. There is presently no
reliable method for physicians to differentiate faked from real pain (Fishbain, 2006).
Naïve human subjects are near chance for differentiating real from fake pain from
observing facial expression (e.g. Hadjistavropoulos et al., 1996). In the absence of
direct training in facial expressions, clinicians are also poor at assessing pain from the
face (e.g. Prkachin et al. 2002; Grossman, 1991). However a number of studies using
the Facial Action Coding System (FACS) (Ekman & Friesen, 1978) have shown that
information exists in the face for differentiating real from posed pain (e.g. Hill and
Craig, 2002; Craig et al., 1991; Prkachin 1992).
In previous studies using manual FACS coding by human experts, at least 12 facial
actions showed significant relationships with pain across multiple studies and pain
modalities. Of these, the ones specifically associated with cold pressor pain were 4, 6,
7, 9, 10, 12, 25, 26 (Craig & Patrick, 1985; Prkachin, 1992). See Table 1 and Figure 2
for names and examples of these AU’s. A previous study compared faked to real
pain, but in a different pain modality (lower back pain). This study found that when
faking subjects tended to display the following AU’s: 4, 6, 7, 10, 12, 25. When faked
pain expressions were compared to real pain expressions, the faked pain expressions
contained significantly more brow lower (AU 4), cheek raise (AU 6), and lip corner

pull (AU 12) (Craig, Hyde & Patrick, 1991). These studies also reported substantial
individual differences in the expressions of both real pain and faked pain.
Recent advances in automated facial expression measurement, such as the CERT
system described above, open up the possibility of automatically differentiating
posed from real pain using computer vision systems (e.g. Bartlett et al., 2006;
Littlewort et al., 2006; Cohn & Schmidt, 2004; Pantic et al., 2006). This section
explores the application of CERT to this problem.

3.1 Human subject methods

Video data was collected of 26 human subjects during real pain, faked pain, and
baseline conditions. Human subjects were university students consisting of 6 men
and 20 women. The pain condition consisted of cold pressor pain induced by
immersing the arm in cold water at 30 Celsius. For the baseline and faked pain
conditions, the water was 200 Celsius. Subjects were instructed to immerse their
forearm into the water up to the elbow, and hold it there for 60 seconds in each of the
three conditions. The order of the conditions was baseline, faked pain, and then real
pain. For the faked pain condition, subjects were asked to manipulate their facial
expressions so that an “expert would be convinced they were in actual pain.”
Participants facial expressions were recorded using a digital video camera during each
condition.
A second subject group underwent the conditions in the counterbalanced order,
with real pain followed by faked pain. This ordering involves immediate motor
memory, which is a fundamentally different task. The present paper therefore
analyzes only the first subject group. The second group will be analyzed separately in
a future paper, and compared to the first group.

Figure 2. Sample facial behavior and facial action codes from the real and
faked pain conditions.

After the videos were collected, a set of 170 naïve observers were shown the
videos and asked to guess whether each video contained faked or real pain. Subjects
were undergraduates with no explicit training in facial expression measurement. They
were primarily Psychology majors at U. Toronto. Mean accuracy of naïve human
subjects for discriminating fake from real pain in these videos was near chance at
52%. These observers had no specific training in facial expression and were not
clinicians. One might suppose that clinicians would be more accurate. However
previous studies suggest that clinicians judgments of pain from the face are similarly
unreliable (e.g. Grossman, 1991). Facial signals do appear to exist however (Hill &
Craig, 2002, Craig et al., 1991; Prkachin 1992), and immediate corrective feedback
has been shown to improve observer accuracy (Hill & Craig, 2004).

3.2 Characterizing the differences between real and faked pain

Applying CERT to the pain video data produced a 20 channel output stream,
consisting of one real value for each learned AU, for each frame of the video. This
data was further analyzed to predict the difference between baseline and pained faces,
and the difference between expressions of real pain and fake pain.
We first examined which facial action detectors were elevated in real pain
compared to the baseline condition. Z-scores for each subject and each AU detector
were computed as Z=(x-µ)/σ, where (µ,σ) are the mean and variance for the output of
frames 100-1100 in the baseline condition (warm water, no faked expressions). The
mean difference in Z-score between the baseline and pain conditions was computed
across the 26 subjects. Table 2 shows the action detectors with the largest difference
in Z-scores. We observed that the actions with the largest Z-scores for genuine pain
were Mouth opening and jaw drop (25 and 26), lip corner puller by zygomatic (12),
nose wrinkle (9), and to a lesser extent, lip raise (10) and cheek raise (6). These facial
actions have been previously associated with cold pressor pain (e.g. Prkachin, 1992;
Craig & Patrick 1985).
The Z-score analysis was next repeated for faked versus baseline. We observed
that in faked pain there was relatively more facial activity than in real pain. The facial

action outputs with the highest z-scores for faked pain relative to baseline were brow
lower (4), distress brow (1 or 1+4), inner brow raise (1), mouth open and jaw drop (25
and 26), cheek raise (6), lip raise (10), fear brow (1+2+4), nose wrinkle (9), mouth
stretch (20), and lower lid raise (7).
Differences between real and faked pain were examined by computing the
difference of the two z-scores. Differences were observed primarily in the outputs of
action unit 4 (brow lower), as well as distress brow (1 or 1+4) and inner brow raise (1
in any combination).

Table 2. Z-score differences of the three pain conditions, averaged across
subjects. FB: Fear brow 1+2+4. DB: Distress brow (1,1+4).

A. Real Pain vs baseline:
Action Unit 25 12 9 26 10 6
Z-score 1.4 1.4 1.3 1.2 0.9 0.9

B. Faked Pain vs Baseline:
Action Unit 4 DB 1 25 12 6 26 10 FB 9 20 7
Z-score 2.7 2.1 1.7 1.5 1.4 1.4 1.3 1.3 1.2 1.1 1.0 0.9

C. Real Pain vs Faked Pain:
Action Unit 4 DB 1
Z-score difference 1.8 1.7 1.0

Table 3. Individual subject differences between faked and genuine pain.
Differences greater than 2 standard deviations are shown. F>P: Number of
subjects in which the output for the given AU was greater in faked than genuine
pain. P>F: Number of subjects for which the output was greater in genuine than
faked pain. FB: Fear brow 1+2+4. DB: Distress brow (1,1+4).

AU 1 2 4 5 6 7 9 10 12 14 15 17 18 20 23 24 25 26 FB DB
F>P 6 4 9 1 7 4 3 6 5 3 5 5 1 4 3 4 4 4 6 5
P>F 3 3 0 0 4 0 4 4 4 2 3 1 3 1 1 1 2 4 2 0

Individual subject differences between faked and real pain are shown in Table 3.
Difference-of-Z-scores between the genuine and faked pain conditions were
computed for each subject and each AU. There was considerable variation among
subjects in the difference between their faked and real pain expressions. However the
most consistent finding is that 9 of the 26 subjects showed significantly more brow
lowering activity (AU4) during the faked pain condition, whereas none of the subjects
showed significantly more AU4 activity during the real pain condition. Also 7
subjects showed more cheek raise (AU6), and 6 subjects showed more inner brow
raise (AU1), and the fear brow combination (1+2+4). The next most common
differences were to show more 12, 15, 17, and distress brow (1 alone or 1+4) during
faked pain.
Paired t-tests were conducted for each AU to assess whether it was a reliable
indicator of genuine versus faked pain in a within-subjects design. Of the 20 actions

tested, the difference was statistically significant for three actions. It was highly
significant for AU 4 (p < .001), and marginally significant for AU 7 and distress brow
(p < .05).
In order to characterize action unit combinations that relate to the difference
between fake and real pain expressions, principal component analysis was conducted
on the difference-of-Z-scores. The first eigenvector had the largest loading on
distress brow and inner brow raise (AU 1). The second eigenvector had the largest
loading on lip corner puller (12) and cheek raise (6) and was lower for fake pain
expressions. The third eigenvector had the largest loading on brow lower (AU 4).
Thus when analyzed singly, the action unit channel with the most information for
discriminating fake from real pain was brow lower (AU 4). However when
correlations were assessed through PCA, the largest variance was attributed to two
combinations, and AU 4 accounted for the third most variance.
Overall, the outputs of the automated system showed similar patterns to previous
studies of real and faked pain using manual FACS coding by human experts.
Exaggerated activity of the brow lower (AU 4) during faked pain is consistent with
previous studies in which the real pain condition was exacerbated lower back pain
(Craig et al. 1991, Hill & Craig, 2002). Another study performed a FACS analysis of
fake and real pain expressions with cold pressor pain, but with children ages 8-12
(LaRochette et al., 2006). This study observed significant elevation in the following
AU’s for fake pain relative to baseline: 1 4 6 7 10 12 20 23 25 26. This closely
matches the AU’s with the highest z-scores in the automated system output of the
present study (Table 2B). LaRochette et al. did not measure AU 9 or the brow
combinations. When faked pain expressions were compared with real cold pressor
pain in children, LaRochette et al found significant differences in AU’s 1 4 7 10.
Again the findings of the present study using the automated system are similar, as the
AU channels with the highest z-scores were 1, 4, and 1+4 (Table 2C), and the t-tests
were significant for 4, 1+4 and 7.

3.3 Automatic Discrimination of Real from Fake Pain

The above analysis examined which AU outputs contained information about
genuine versus faked pain. We next turned to the problem of discriminating genuine
from faked pain expressions in a subject-independent manner. If the task were to
simply detect the presence of a red-flag set of facial actions, then differentiating fake
from real pain expressions would be relatively simple. However, it should be noted
that subjects display actions such as AU 4, for example, in both real and fake pain,
and the distinction is in the quantity of AU 4. Also, there is inter-subject variation in
expressions of both real and fake pain, there may be combinatorial differences in the
sets of actions displayed during real and fake pain, and the subjects may cluster. We
therefore applied machine learning to the task of discriminating real from faked pain
expressions in a subject-independent manner.
A second-layer classifier was trained to discriminate genuine pain from faked pain
based on the 20-channel output stream. For this second-layer classification step, we
explored SVM’s, Adaboost, and linear discriminant analysis. Nonlinear SVM’s with
radial basis function kernels gave the best performance. System performance for

generalization to novel subjects was assessed using leave-one-out cross-validation, in
which all the data from one subject was reserved for testing on each trial.
Prior to learning, the system performed an automatic reliability estimate based on
the smoothness of the eye positions, and those frames with low reliability were
automatically excluded from training and testing the real pain / fake pain classifier.
Those frames with abrupt shifts of 2 or more pixels in the returned eye positions were
automatically detected and labeled unreliable. This tends to occur during eyeblinks
with the current eye detector. However future versions of the eye detector will
correct that issue. The reliability filter had a relatively small effect on performance.
The analysis of Table 2 was repeated under this criterion, and the Z-scores improved
by about 0.1. Note also that the reliability filter on the frames is not to be confused
with dropping the difficult trials since a real pain / fake pain decision was always
made for each subject.
The 60 second video from each condition was broken up into 6 overlapping
segments of 500 frames. For each segment, the following 5 statistics were measured
for each of the 20 AU’s: median, maximum, range, first to third quartile difference,
90 to 100 percentile difference. Thus the input to the SVM for each segment
contained 100 dimensions. Each cross-validation trial contained 300 training samples
(25 subjects x 2 conditions x 6 segments).
A nonlinear SVM trained to discriminate posed from real facial expressions of pain
obtained an area under the ROC of .72 for generalization to novel subjects. This was
significantly higher than performance of naïve human subjects, who obtained a mean
accuracy of 52% correct for discriminating faked from real pain on the same set of
videos.

3.3 Comparison with a Human Expert

In order to assess the validity of the system findings, we obtained FACS codes for
a portion of the video from a human FACS expert certified in the Facial Action
Coding System. For each subject, the last 500 frames of the fake pain and real pain
conditions were FACS coded (about 15 seconds each). It took 60 man hours to
collect the human codes, over the course of more than 3 months, since human coders
can only code up to 2 hours per day before having negative repercussions in accuracy
and coder burn-out.
The sum of the frames containing each action unit were collected for each subject
condition, as well as a weighted sum, multiplied by the intensity of the action on a 1-5
scale. To investigate whether any action units successfully differentiated real from
faked pain, paired t-tests were computed on each individual action unit. (Tests on
specific brow region combinations 1+2+4 and 1,1+4 have not yet been conducted.)
The one action unit that significantly differentiated the two conditions was AU 4,
brow lower, (p<.01) for both the sum and weighted sum measures. This finding is
consistent with the analysis of the automated system, which also found action unit 4
most discriminative.

3.4 Discussion of pain study

The field of automatic facial expression analysis has advanced to the point that we
can begin to apply it to address research questions in behavioral science. Here we
describe a pioneering effort to apply fully automated facial action coding to the
problem of differentiating fake from real expressions of pain. While naïve human
subjects were only at 52% accuracy for distinguishing fake from real pain, the
automated system obtained .72 area under the ROC, which is equivalent to 72%
correct on a 2-alternative forced choice. Moreover, the pattern of results in terms of
which facial actions may be involved in real pain, fake pain, and differentiating real
from fake pain is similar to previous findings in the psychology literature using
manual FACS coding.
Here we applied machine learning on a 20-channel output stream of facial action
detectors. The machine learning was applied to samples of spontaneous expressions
during the subject state in question. Here the state in question was fake versus real
pain. The same approach can be applied to learn about other subject states, given a
set of spontaneous expression samples. For example, we recently developed a related
system to detect driver drowsiness from facial expression (Vural et al., 2007).
While the accuracy of individual facial action detectors is still below that of human
experts, automated systems can be applied to large quantities of video data. Statistical
pattern recognition on this large quantity of data can reveal emergent behavioral
patterns that previously would have required hundreds of coding hours by human
experts, and would be unattainable by the non-expert. Moreover, automated facial
expression analysis will enable investigations into facial expression dynamics that
were previously intractable by human coding because of the time required to code
intensity changes. Future work in automatic discrimination of fake and real pain will
include investigations into facial expression dynamics.

4 Automatic Detection of Driver Fatigue
The US National Highway Trafﬁc Safety Administration estimates that in the US
alone approximately 100,000 crashes each year are caused primarily by driver drowsi-
ness or fatigue [2]. Thus incorporating automatic driver fatigue detection mechanism
into vehicles may help prevent many accidents.
One can use a number of different techniques for analyzing driver exhaustion.
One set of techniques places sensors on standard vehicle components, e.g., steering
wheel, gas pedal, and analyzes the signals sent by these sensors to detect drowsiness
[3]. It is important for such techniques to be adapted to the driver, since Abut and his
colleagues note that there are noticeable differences among drivers in the way they
use the gas pedal [4].
A second set of techniques focuses on measurement of physiological signals
such as heart rate, pulse rate, and Electroencephalography (EEG) [5]. It has been re-
ported by re-searchers that as the alertness level decreases EEG powerof the alpha
and theta bands increases [6]. Hence providing indicators of drowsiness. However
this method has draw-backs in terms of practicality since it requires a person to wear
an EEG cap while driving.

A third set of solutions focuses on computer vision systems that can detect and
recognize the facial motion and appearance changes occurring during drowsiness [7]
[8].The advantage of computer vision techniques is that they are non-invasive, and
thus are more amenable to use by the general public. There are some significant previ -
ous studies about drowsiness detection using computer vision tech-niques. Most of
the published research on computer vision approaches to detection of fatigue has fo-
cused on the analysis of blinks and head movements. However the effect of drowsi-
ness on other facial expressions have not been studied thoroughly. Recently Gu & Ji
presented one of the first fatigue studies that incorporates certain facial expressions
other than blinks. Their study feeds action unit information as an input to a dynamic
Bayesian network. The network was trained on subjects posing a state of fatigue [9].
The video segments were classified into three stages: inattention, yawn, or falling
asleep. For predicting falling asleep, head nods, blinks, nose wrinkles and eyelid
tighteners were used.
Previous approaches to drowsiness detection primarily make pre-assumptions
about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here
we em-ploy machine learning methods to datamine actual human behavior during
drowsiness episodes. The objective of this study is to discover what facial configura-
tions are predictors of fatigue. In this study, facial motion was analyzed automatically
from video using a fully automated facial expression analysis system based on the Fa-
cial Action Coding System (FACS) (Ekman & Friesen, 1978). In addition to the out-
put of the automatic FACS recognition system we also collected head motion data us-
ing an accelerometer placed on the subject’s head, as well as steering wheel data.

4.1 Driving task

Subjects played a driving video game on a windows machine using a steering
wheel1and an open source multi- platform video game2(See Figure 1). At random
times, a wind effect was applied that dragged the car to the right or left, forcing the
subject to correct the position of the car. This type of manipulation had been found in
the past to increase fatigue [11]. Driving speed was held constant. Four subjects
performed the driving task over a three hour period beginning at midnight. During
this time subjects fell asleep multiple times thus crashing their vehicles. Episodes in
which the car left the road (crash) were recorded. Video of the subjects face was
recorded using a DV camera for the entire 3 hour session.
In addition to measuring facial expressions with CERT, head movement was
measured using an accelerometer that has 3 degrees of freedom. This three
dimensional accelerometer3has three one dimensional accelerometers mounted at right
angles measuring accelerations in the range of 5g to +5g where g represents earth
gravitational force.

Fig.1 Driving Simulation Task.

1ThrustmasterRFerrariRacing Wheel
2The Open Racing Car Simulator (TORCS)

4.2 Facial Actions Associated with Driver Fatigue

Subject data was partitioned into drowsy (non-alert) and alert states as follows. The
one minute preceding a sleep episode or a crash was identified as a non-alert state.
There was a mean of 24 non-alert episodes with a minimum of 9 and a maximum of
35. Fourteen alert segments for each subject were collected from the first 20 minutes
of the driving task.
The output of the facial action detector consisted of a continuous value for each
facial action and each video frame which was the distance to the separating
hyperplane, i.e., the margin. Histograms for two of the action units in alert and non-
alert states are shown in Figure 4. The area under the ROC (A’) was computed for the
outputs of each facial action detector to see to what degree the alert and non-alert
output distributions were separated.

Fig. 4 Example histograms for blink and Action Unit 2 in alert and non-alert states for
one subject. A’ is area under the ROC.

In order to understand how each action unit is associated with drowsiness across
different subjects, Multinomial Logistic Ridge Regression (MLR) was trained on each
facial action individually. Examination of the A’ for each action unit reveals the
degree to which each facial movement was able to predict drowsiness in this study.
The A’s for the drowsy and alert states are shown in Table 4. The five facial actions
that were the most predictive of drowsiness by increasing in drowsy states were 45, 2
(outer brow raise), 15 (frown), 17 (chin raise), and 9 (nose wrinkle). The five actions
that were the most predictive of drowsiness by decreasing in drowsy states were 12
(smile), 7 (lid tighten), 39 (nostril compress), 4 (brow lower), and 26 (jaw drop). The
high predictive ability of the blink/eye closure measure was expected. However the
predictability of the outer brow raise (AU 2) was previously unknown.
We observed during this study that many subjects raised their eyebrows in an
attempt to keep their eyes open, and the strong association of the AU 2 detector is
consistent with that observation. Also of note is that action 26, jaw drop, which
occurs during yawning, actually occurred less often in the critical 60 seconds prior to
a crash. This is consistent with the prediction that yawning does not tend to occur in
the final moments before falling asleep.

Table 5 MLR model for predicting drowsiness across subjects. Predictive performance of each
facial action individually is shown.
More when critically drowsy Less when critically drowsy
AU Name A’
45 Blink/Eye Closure 0.94 AU Name A’
2 Outer Brow Raise 0.81 12 Smile 0.87
15 Lip Corner Depressor 0.80 7 Lid tighten 0.86
17 Chin Raiser 0.79 39 Nostril Compress 0.79
9 Nose Wrinkle 0.78 4 Brow lower 0.79
30 Jaw Sideways 0.76 26 Jaw Drop 0.77
20 Lip stretch 0.74 6 Cheek Raise 0.73
11 Nasolabial Furrow 0.71 38 Nostril Dilate 0.72
14 Dimpler 0.71 23 Lip tighten 0.67
1 Inner Brow Raise 0.68 8 Lips toward 0.67
10 Upper Lip Raise 0.67 5 Upper lid raise 0.65
27 Mouth Stretch 0.66 16 Upper lip depress 0.64
18 Lip Pucker 0.66 32 Bite 0.63
22 Lip funneler 0.64
24 Lip presser 0.64
19 Tongue show 0.61

4.3 Automatic Detection of Driver Fatigue

The ability to predict drowsiness in novel subjects from the facial action code was
then tested by running MLR on the full set of facial action outputs. Prediction
performance was tested by using a leave-one-out cross validation procedure, in which
one subjects’ data was withheld from the MLR training and retained for testing, and
the test was repeated for each subject. The data for each subject by facial action was
first normalized to zero-mean and unit standard deviation. The MLR output for each
AU feature was summed over a temporal window of 12 seconds (360 frames) before
computing A’.
MLR trained on all AU features obtained an A’ of .90 for predicting drowsiness in
novel subjects. Because prediction accuracy may be enhanced by feature selection, in
which only the AU’s with the most information for discriminating drowsiness are
included in the regression, a second MLR was trained by contingent feature selection,
starting with the most discriminative feature (AU 45), and then iteratively adding the
next most discriminative feature given the features already selected. These features
are shown at the bottom of Table 4. Best performance of .98 was obtained with five
features: 45, 2, 19 (tongue show), 26 (jaw drop), and 15. This five feature model
outperformed the MLR trained on all features.

Table 4 Drowsiness detection performance for novel subjects, using an MLR classifier with
different feature combinations. The weighted features are summed over 12 seconds before
computing A’.
Feature A’

AU45,AU2,AU19,AU26,AU1 .9792
5 .8954
all the features

Effect of Temporal Window Length. We next examined the effect of the size of the
temporal window on performance. The five feature model was employed for this
analysis. The performances shown in Table 4 employed a temporal window of 12
seconds. Here, the MLR output in the 5 feature model was summed over windows of
N seconds, where N ranged from 0.5 to 60 seconds. Figure 5 shows the area under the
ROC for drowsiness detection in novel subjects over time periods. Performance
saturates at about 0.99 as the window size exceeds 30 seconds. In other words, given
a 30 second video segment the system can discriminate sleepy versus non-sleepy
segments with 0.99 accuracy across subjects.

Fig. 5 Performance for drowsiness detection in novel subjects over temporal window
sizes. Red point indicates the priorly obtained performace for a temporal window of 12
seconds.

4.4 Coupling of Behaviors

Coupling of steering and head motion. Observation of the subjects during drowsy
and nondrowsy states indicated that the subjects head motion differed substantially
when alert versus when the driver was about to fall asleep. Surprisingly, head motion
increased as the driver became drowsy, with large roll motion coupled with the
steering motion as the driver became drowsy. Just before falling asleep, the head
would become still.
We also investigated the coupling of the head and arm motions. Correlations
between head motion as measured by the roll dimension of the accelerometer output
and the steering wheel motion are shown in Figure 6. For this subject (subject 2), the
correlation between head motion and steering increased from 0.33 in the alert state to
0.71 in the non-alert state. For subject 1, the correlation between head motion and
steering similarly increased from 0.24 in the alert state to 0.43 in the non-alert state.
The other two subjects showed a smaller coupling effect. Future work includes
combining the head motion measures and steering correlations with the facial
movement measures in the predictive model.

Fig. 6 Head motion (blue/gray) and steering position (red/black) for 60 seconds in an alert state
(left) and 60 seconds prior to a crash (right). Head motion is the output of the roll dimension of
the accelerometer.

Coupling of eye openness and eyebrow raise. We observed that for some of the
subjects coupling between eye brow up’s and eye openness increased in the drowsy
state. In other words subjects tried to open their eyes using their eyebrows in an
attempt to keep awake.

Fig. 7 Eye Openness and Eye Brow Raises (AU2) for 10 seconds in an alert state (left) and
10 seconds prior to a crash (right).

4.4 Conclusions of Driver Fatigue Study

This chapter presented a system for automatic detection of driver drowsiness from
video. Previous approaches focused on assumptions about behaviors that might be
predictive of drowsiness. Here, a system for automatically measuring facial

expressions was employed to datamine spontaneous behavior during real drowsiness
episodes. This is the first work to our knowledge to reveal significant associations
between facial expression and fatigue beyond eyeblinks. The project also revealed a
potential association between head roll and driver drowsiness, and the coupling of
head roll with steering motion during drowsiness. Of note is that a behavior that is
often assumed to be predictive of drowsiness, yawn, was in fact a negative predictor
of the 60-second window prior to a crash. It appears that in the moments before
falling asleep, drivers yawn less, not more, often. This highlights the importance of
using examples of fatigue and drowsiness conditions in which subjects actually fall
sleep.

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References

1. Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library
Metadata Architecture. Int. J. Digit. Libr. 1 (1997) 108–121
2. Bruce, K.B., Cardelli, L., Pierce, B.C.: Comparing Object Encodings. In: Abadi, M., Ito, T.
(eds.): Theoretical Aspects of Computer Software. Lecture Notes in Computer Science, Vol.
1281. Springer-Verlag, Berlin Heidelberg New York (1997) 415–438
3. van Leeuwen, J. (ed.): Computer Science Today. Recent Trends and Developments. Lecture
Notes in Computer Science, Vol. 1000. Springer-Verlag, Berlin Heidelberg New York
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4. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn.
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