Psychology
2013. Vol.4, No.8, 663-668
Published Online August 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.48094
Static and Dynamic Presentation of Emotions in Different Facial
Areas: Fear and Surprise Show Influences of Temporal and
Spatial Properties*
Holger Hoffmann1, Harald C. Traue1, Kerstin Limbrecht-Ecklundt1,
Steffen Walter1, Henrik Kessler2
1Medical Psychology, University of Ulm, Ulm, Germany
2Medical Psychology, University of Bonn, Bonn, Germany
Received November 29th, 2012; revised January 6th, 2013; accepted February 6th, 2013
Copyright © 2013 Holger Hoffmann et al. This is an open access article distributed under the Creative Com-
mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro-
vided the original work is properly cited.
For the presentation of facially expressed emotions in experimental settings a sound knowledge about
stimulus properties is pivotal. We hence conducted two experiments to investigate the influence of tem-
poral (static versus dynamic) and spatial (upper versus lower half of the face) properties of facial emotion
stimuli on recognition accuracy. In the first experiment, different results were found for the six emotions
examined (anger, disgust, fear, happiness, sadness and surprise). Fear and surprise were more accurately
recognized when using dynamic stimuli. In the second experiment using only dynamic presentations,
recognition rates between upper and lower face varied significantly for most emotions with fear and hap-
piness only being detectable in the upper or lower half respectively. The results suggest an emotion-spe-
cific effect for the importance of the facial area.
Keywords: Emotion Recognition; Facial Expressions; Static vs. Dynamic
Introduction
The recognition of emotions is an essential element of social
interaction (Buck, 1984; Ekman, 1993). A common paradigm
for studying the ability of emotion recognition is the presenta-
tion of photographs with different emotional expressions, usu-
ally taken at the time of the strongest expression. The disad-
vantage of this approach is that the ecological validity of such
stimuli must be regarded as limited. This is due to the fact that
during real-world interactions people must recognize emotions
from dynamically changing faces. If one observes emotions
over time, it is obvious that they arise at a specific moment,
reach their peak and subside again (Onset, Apex, Offset; Hess
& Kleck, 2005). In interactions one can also observe that emo-
tions only appear in some areas of the face. The use of static
stimuli thus implies the danger of not capturing actual recogni-
tion accuracy due to the lack of dynamics and a potential un-
derestimation of the importance of specific facial areas. Hence,
it can be concluded that the use of dynamic sequences better
reproduces real life situations of emotion recognition and thus
allows for more accurate results regarding the recognition of
emotions. This could be reflected, for example, in better esti-
mates of recognition accuracy. In the 1980s, Ekman & Friesen
(1982) already suspected that static and dynamic stimulus ma-
terial may result in performance differences.
Another argument in favor of using dynamic stimuli lies in
the brain areas being differentially active when presenting static
versus dynamic emotional expressions (Trautmann, Fehr, &
Herrmann, 2009; Kessler, Doyen-Waldecker, Hofer, Hoffmann,
Traue, & Abler, 2011). Additionally, if natural processes are to
be examined, which is not part of this study, dynamic stimuli
should be used (e.g., Kilts, Egan, Gideon, Ely, & Hoffman,
2003; LaBar, Crupain, Voyvodic, & McCarthy, 2003; Sato,
Kochiyama, Yoshikawa, Naito, & Matsumara, 2004). However,
these dynamic stimuli should be standardized, allowing for
comparison of results between different studies. The influence
of dynamic emotion expressions was already examined in detail
in a number of studies with inconsistent results. Harwood, Hall,
& Shinkfield (1999) showed, for example, that anger and sad-
ness were more readily recognized when the emotion was pre-
sented dynamically. In two classic studies by Bassili (1978,
1979) an improvement in emotion recognition was demon-
strated for all emotions when using dynamic stimuli, however.
Ambadar, Schooler, & Cohn (2005) used single-static, multi-
static, and dynamic stimuli and could demonstrate a robust
effect of motion and suggested that this effect was due to the
dynamic property of the expression. Additionally, Trautmann,
Fehr, & Herrmann (2009) found differences in brain activation
patterns comparing the neural processing of static and dynamic
stimuli. In their study dynamic stimuli revealed a better recog-
nizability than static stimulus material. A review published in
2013 by Krumhuber, Kappas, & Manstead pronounces the
limitation of static stimuli and underline the dynamic nature of
*This research was supported in part by grants from the Transregional Col-
laborative Research Centre SFB/TRR 62 “Companion-Technology for Cog-
nitive Technical Systems” funded by the German Research Foundation
(DFG).

H. HOFFMANN ET AL.
facial activity. On the other side, Wehrle, Kaiser, Schmidt, &
Scherer (2000) found no statistically significant differences be-
tween static and dynamic facial expressions as well as Fioren-
tini & Viviani (2011).
It is not always the entire face that provides important clues
about the expressed emotion. One way of discovering which
parts of the face are subjected to a more detailed analysis dur-
ing the processes of emotion recognition, is by dividing the face
into an upper and a lower half and presenting an emotional ex-
pression in only one of the areas. Previous studies (Calder,
Young, Keane, & Dean, 2000; Bassili, 1978; Bassili, 1979)
suggest that emotion recognition is not the same for all basic
emotions. This means that there appear to be specific key stim-
uli for each emotion, which provide the basis for classification.
For example, recognition of surprise is associated with observ-
ing wide-open eyes. A disgusted face, however, is characterized
by a wrinkled nose and lifting of the upper lip. It can thus be
assumed that the relevance of the specific half of the face de-
pends on the respective basic emotion and its associated key
stimuli.
Our study therefore has two aims: 1) Evaluating possible
differences between the use of dynamic versus static stimulus
material and 2) assessing differential contributions of the upper
versus lower half of a facial expression to recognition accuracy.
General Methods
The stimuli used for this study were pictures from the
JACFEE/JACNeuF (Japanese and Caucasian Facial Expres-
sions of Emotion and Neutral Faces) picture set (Matsumoto &
Ekman, 1988). This is a picture set consisting of 56 actors por-
traying one of seven emotions (anger, contempt, disgust, fear,
happiness, sadness and surprise). Half of the actors are male,
half female; half are of Japanese and half of Caucasian origin.
For our experiments, a subset of 42 actors and six emotions was
used, evenly distributed among all sub-sets of stimuli. Con-
tempt was excluded, because this emotion is not considered in
the vast majority of studies in the field. In our picture set one
actor displays only one emotional and one neutral expression.
Several studies have shown the reliability and validity of the
JACFEE/JACNeuF picture set in displaying the intended emo-
tions (e.g., Biehl, Matsumoto, Ekman, Hearn, Heider, Kudoh,
& Ton, 1997).
The FEMT (Facial Expression Morphing Tool) was used for
creating the subtle facial expressions employed in these ex-
periments (Kessler, Hoffmann, Bayerl, Neumann, Basic, Deigh-
ton, & Traue, 2005; see also Hoffmann, Kessler, Eppel, Ru-
kavina, & Traue, 2010). This software uses different morphing
algorithms to produce intermediate frames between two images.
This method was optimized by implementing additional tech-
niques. Sequences were generated using multiple layers that
minimized distracting facial information by only morphing the
important feature of the face. The use of the multiple layers and
special smoothing algorithms allowed us to create realistic tran-
sitions from closed to open mouths, for example. The FEMT
can generate images in any intensity between 0% (neutral face)
and 100% (full-blown emotion). All stimuli used in the study
were in color and presented on a computer screen.
Experiment 1
In this experiment, the recognition accuracies for static and
dynamic stimuli were compared. The hypothesis was that sta-
tistically significant differences exist between static and dy-
namic stimuli and for the six basic emotions.
Methods
Participants
The study included N = 220 healthy participants. The age of
the participants of the experimental group (EG; N = 110)
ranged from 18 to 28 years (M = 20.5; SD = 2.0). 70 study
participants were female (63.6%), 40 male (36.4%). All sub-
jects of the experimental group gave their written consent to
participate in the experiment. A control group (CG; N = 110)
was then matched from the FEEL database. The age of the par-
ticipants in the control group ranged from 19 to 29 years (M =
21.5; SD = 2.3), 63.6% of them were female.
The FEEL Test (Facially Expressed Emotion Labeling)
The FEEL test is a computer-based method for measuring
individual emotion recognition ability (Kessler, Bayerl, Deigh-
ton, & Traue, 2002). It consists of pictures of 42 different actors
portraying the six basic emotions (happiness, sadness, disgust,
fear, surprise, anger). These images were taken from the
JACFEE/JACNeuF picture set (Matsumoto & Ekman, 1988),
which was described above. After showing a neutral facial ex-
pression, the emotional facial expressions are presented on the
computer screen for 300 ms before they must be assigned to a
category. For this, a choice box appears from which a selection
can be made by clicking on one of the six emotions (forced-
choice format). A total of 48 images are presented, as each
emotion is shown in a trial run beforehand, so the subjects can
acquaint themselves with the task. With a Cronbach’s alpha of r
= .77 the test has a high reliability. In the period in which the
FEEL test was successfully used, data from 600 healthy sub-
jects of different age, sex and education were collected, so that
user-defined control groups can be prepared using this database.
Different issues have been examined with this approach (e.g.,
Hoffmann et al., 2010; Kessler, Roth, von Wietersheim, Deigh-
ton, & Traue, 2007).
Procedure
The subjects selected from the FEEL database saw static
images with a full-blown emotional expression. The experi-
mental group, however, was presented with video sequences
that were created from the respective neutral and emotional
images, using the FEMT. Since the quality of the picture mate-
rial only allowed the creation of 36 video sequences, data from
the control group were adjusted to account for the missing
stimuli. All subjects had to complete six trial runs before the
actual test, to ensure familiarity with the procedure. As can be
seen in Figure 1, the test procedure was designed to be as iden-
tical as possible for the two groups. First, all participants were
presented with the neutral expression of an actor. While the
control group saw the neutral face for 1500 ms, the experimen-
tal group saw it for 1300 ms to 2100 ms. The difference in the
presentation time of the neutral face is due to the fact that the
dynamic sequences following it had a length of between 400 ms
(surprise) and 1200 ms (sadness), depending on the emotion
shown. In order to perceive the development of an emotion as
natural, a particular temporal sequence must be created. For the
emotion of surprise e.g., a much shorter time for the onset is
considered as natural compared to the emotion of sadness
664

H. HOFFMANN ET AL.
Figure 1.
Static vs. dynamic presentation of facial expressions. The left side represents the experimental
group (dynamic condition); the right side represents the FEEL group (static condition).
(Hoffmann, Traue, Bachmayr, & Kessler, 2010).
While the experimental group saw the dynamic sequence, the
control group was presented with a white screen. Both groups
subsequently saw the full-blown emotion for 300 ms, so that for
all participants each trial lasted 2500 ms in total. Once the emo-
tional image disappeared from the computer screen, six choice
boxes with one emotion label each appeared after 500 ms. Sub-
jects had to choose by mouse click which emotion they had just
seen. The participants had ten seconds for deciding before the
next trial started. The images with the emotional expressions
and the six choice boxes were presented at different times.
Presentation of the images was randomized.
Results
Experiment 1 had a 6 × 2 × 2 mixed design. The within-
subject factor was emotion (anger, disgust, fear, happiness,
sadness or surprise). The between-subject factors were partici-
pants’ gender (female or male) and the type of stimulus mate-
rial presented (static or dynamic). Analyses were performed
using the SPSS 20 software package. Generally, there was no
statistical difference in the recognition accuracy between static
(M = 82.5%; SD = 9.3) and dynamic (M = 83.7%; SD = 8.2)
stimulus material. Recognition rates for the six emotions dif-
fered significantly (F(5,213) = 47.92; p < .001) and are shown in
Table 1. The interaction between emotion and type of stimulus
material was significant, too (F(5,213) = 5.17; p < .001), meaning
that the type of stimulus material presented influences the rec-
ognition accuracy for the six emotions in a different way. We
therefore decided to analyze the results looking at the different
basic emotions. The results showed that recognition accuracy
for surprise (Mstat = 83.9%; Mdyn = 90.2%; p < .01) and fear
(Mstat = 67.9%; Mdyn = 75.8%; p < .05) increased significantly
when presented dynamically as opposed to a static display. In
contrast, the recognition accuracy for happiness is statistically
higher when using static instead of dynamic stimulus material
(Mstat = 96.4%; Mdyn = 94.1%; p < .05). No significant differ-
ences were observed for the emotions anger, disgust and sad-
ness when comparing the experimental and control groups.
Female (M = 83.2%) and male (M = 82.9%) participants per-
formed equally.
Discussion
The conducted experiment partially confirmed the hypothesis
that dynamic and static presentations result in significant dif-
ferences for the individual emotions, although no overall sig-
nificant difference was found for the use of static and dynamic
picture material. The absence of a general effect of display
condition over all emotions is consistent with the results of
Ambadar et al. (2005), according to which dynamic sequences
could not significantly increase recognition accuracy in com-
parison to a first-last condition.
A differentiated comparison (dynamic versus static) showed
that fear and surprise were more readily recognized when the
subjects were presented with dynamic sequences. This contra-
dicts the results of a study by Harwood et al. (1999), which
reported that the emotions of anger and sadness particularly
profited from dynamic presentation. The discrepancy might be
explained by the choice of other stimuli. Happiness was less
well recognized when presented dynamically, a result that con-
tradicts previous studies. Fear and surprise, on the other hand,
tend to be recognized twice as easy when using dynamic se-
quences. Fear and surprise are often confused, presumably be-
cause of the high similarity of the facial expressions (eyes wide
open). It appears that these mix-ups can be reduced by means of
the additional information provided during the dynamic emer-
gence of the emotion. We assume that the information gained
from movement of the mouth and eyes provides particularly
important clues for a correct recognition (Jack, Garrod, Yu,
Caldara, & Schyns, 2012). The opposite seems to be the case
for the recognition of happiness. When quantifying mix-ups, it
was striking that in the dynamic presentation condition this
emotion was frequently confused with disgust. It is possible
that subjects focus on the raising of the lip, which occurs in
case of happiness and disgust, so much that other differentiated
information, such as wrinkling the nose in case of disgust or the
activation of the M. orbicularis occuli in case of happiness, are
not considered sufficiently. Contrary to this, other work has
shown that spontaneous and deliberate smiles could be distin-
guished from each other on the basis of dynamic displays, but
not static ones (Krumhuber & Manstead, 2009) indicating that
the dynamic presentation of happiness increases the ability to
distinguish happiness from other emotional states.
Referring to the results of Fiorentini and Viviani (2011), who
did not find an advantage for dynamic stimulus material, one
should consider the method to develop dynamic stimuli. The
authors used high-speed recordings of actors’ facial expressions
not morphing sequences of a neutral and a full-blown expres-
sion. This may explain the differences in the results and en-
courage discussing how dynamic stimulus material should be
created—with natural expressions or derived from static mate-
rial. Both options have their (dis-)advantages and cannot be
discussed here.
In conclusion, although results are not clear-cut according to
Trautmann et al. (2009) dynamic facial expressions might im-

H. HOFFMANN ET AL.
Table 1.
Recognition accuracy for the different emotion categories.
Stimulus type
Emotion
Static Dynamic Effect
Anger 91.4 (14.4) 90.5 (14.7) n.s.
Disgust 74.4 (25.8) 70.0 (23.7) n.s.
Fear 67.9 (25.8) 75.8 (22.9) p < .05
Happiness 96.4 (7.3) 94.1 (8.3) p < .05
Sadness 80.9 (20.4) 81.8 (20.3) n.s.
Surprise 83.9 (15.0) 90.2 (14.0) p < .01
Total 82.5 (9.3) 83.7 (8.2) n.s.
Note: Standard errors are in parentheses. Recognition accuracy values are in per-
cent.
prove the model how facial expression is processed in humans.
Experiment 2
Emotion recognition accuracy using information from only
the lower or the upper part of the face was compared in a sec-
ond experiment. It was assumed that recognition accuracy dif-
fers significantly for these two conditions.
Methods
Participants
The participants were N = 57 students at Ulm University,
who gave written consent for participation in the study. Their
ages ranged from 18 to 25 years (M = 20.4; SD = 1.6). 42 study
participants were female (73.7%), 15 male (26.3%). None of
them had been tested in Experiment 1.
Stimuli
In Experiment 2, the facial expressions of the dynamic se-
quences were synthesized in such a fashion that the transforma-
tion was visible only in the upper or the lower half. For this
purpose, the face was divided into two halves and a video se-
quence was generated for each image pair in which the change
from a neutral to an emotional expression took place either in
the upper or the lower half of the face. This resulted in 72 se-
quences (6 emotions × 6 sequences × 2 areas of the face). The
division of the face into an upper and a lower part was based on
the inherent anatomy of the face and can be seen in Figure 2.
Procedure
As in Experiment 1, the subjects first had to complete six
trial runs before the actual experiment began. Subsequently
they were presented with the 72 sequences in randomized order.
Six sequences were presented for each emotion; 50% of the
sequences displayed a change in the lower half of the face, and
50% of the sequences displayed a change in the upper half of
the face. The course of the trial runs corresponded to that in
Experiment 1, and the subsequent evaluation and selection of
the emotion shown also followed the same experimental design.
One difference between the experiments is the use of a seventh
choice box labeled “not recognized” in Experiment 2, which
could be selected when the subject was not able to assign the
facial expression to one of the six emotions. This was intended
Figure 2.
Division of the face into an upper and a
lower part. The upper area includes the
following anatomical regions of the face:
Regio frontalis, Regio orbitalis, Regio
nasalis. The lower area includes: Regio
oralis, Regio buccalis, Regio infraorbi-
talis, Regio zygomatica, Regio mentalis
& Regio temporalis.
to prevent random assignment to an emotion due to lack of
choices.
Results
For the statistical analysis we used a generalized linear
model considering the dependency structure of our data. Ex-
periment 2 had a 6 × 2 × 2 mixed design with emotion (happi-
ness, disgust, anger, fear, surprise or sadness) and type of
stimulus presentation (upper or lower part of the face) as
within-subject factors and the gender of the participants (male
or female) as a between-subject factor.
Recognition rates differed significantly between the six emo-
tions (Wald χ2 (5, N = 57) = 110.50; p < .001) and are shown in
Table 2. As hypothesized, results show that recognition accu-
racy differed for the two presentation types, Wald χ2 (1, N = 57)
= 29.2; p < .001. The interaction between emotion and type of
stimulus presentation was also significant (Wald χ2 (5, N = 57)
= 139.16; p < .001). Disgust, happiness and sadness were rec-
ognized better when the emotional expression was shown in the
lower part of the face (p < .001). Surprise (p < .05) and fear (p
< .001) in contrast were recognized better when a dynamic
change was presented in the upper part of the face. The recog-
nition accuracy of anger seems to be independent of the pres-
entation mode. Subjects were not able to recognize fear and
happiness above chance when changes were exclusively pre-
sented in the lower half of the face or only in the upper part of
the face, respectively. In the case of surprise, on the other hand,
high hit rates for both conditions (72.5% in the lower face and
83.0% in the upper face) could be observed. Female and male
participants performed equally, no gender effect could be ob-
served.
Discussion
Our hypothesis that presentation of dynamic changes in the
upper versus lower part of the face has a differential impact on
emotion recognition was partially confirmed. Fear was only
reliably detected when the upper half of the face was presented.
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H. HOFFMANN ET AL.
Table 2.
Recognition accuracy for the different emotion categories.
Part of the Face
Emotion
Upper Lower Effect
Anger 59.1 (28.8) 67.3 (35.3) n.s.
Disgust 41.5 (32.9) 62.0 (28.5) p < .001
Fear 50.9 (31.6) 15.2 (28.2) p < .001
Happiness 18.1 (22.8) 97.1 (11.4) p < .001
Sadness 43.9 (34.6) 63.7 (27.7) p < .001
Surprise 83.0 (24.5) 72.5 (29.0) p < .05
Note: Standard errors are in parentheses. Recognition accuracy values are in per-
cent.
In the case of happiness the opposite was true. Surprise was
almost equally well recognized in both conditions. With regard
to the emotions of happiness and surprise, these results are
largely consistent with the studies by Bassili (1978, 1979) and
Calder et al. (2000). In the aforementioned studies, surprise was
always recognized at a rate of over 70%, regardless of whether
the emotional expression was presented in the upper or lower
half of the face. Contradictory results are found for anger and
sadness. In the present study and the studies by Bassili (1978,
1979) no differences were found for the presentation of anger in
the upper or lower part of the face. Calder et al. (2000) showed
significantly better recognition accuracy for the presentation of
anger in the upper half of the face. In the study by Calder, sad-
ness was recognized when presented in the lower half of the
face, in the other studies, however, when presented in the upper
half. The differences can possibly be explained by different
sample sizes (significantly larger in our study) or the division
of the face. While the present research used a facial division
based on anatomical features, the abovementioned studies util-
ized a geometric facial division. Furthermore, in the older stud-
ies, only one half of the face was presented, the other half was
hidden. This does not conform to real life conditions. The pre-
sent study showed the entire face. Overall, it could be shown
that certain areas of the face are of different relevance for the
recognition of basic emotions, though further research is re-
quired.
General Discussion
The results provide new information regarding the question
of the ecological validity of stimulus material in the study of
emotion recognition. Experiment 1 showed no differences in
the use of dynamic and static stimulus material over all emo-
tions, but interesting effects on the level of individual emotions.
Experiment 2 showed that the recognition of emotions is dif-
ferentially influenced by the presentation of the expression in
the upper or lower half of the face.
The application of dynamic stimuli is hence not necessary for
capturing the assessment of emotion recognition in general.
Nevertheless, it appears that dynamic information improves the
recognition rates for some emotions. This finding is contra-
dicted by our results regarding the emotion of happiness. Here,
higher recognition accuracy was achieved with the use of static
stimuli. One must take into account, however, that the recogni-
tion rates for this emotion tend to be very high, and that such a
result could therefore be due to ceiling effects. Apart from the
fact that dynamic stimuli closely represent the natural occur-
rence of facial emotions, the notion of different brain areas
being active when perceiving static versus dynamic stimuli
argues in favor of an application of the latter.
Furthermore, emotion recognition appears to depend on the
perception of different areas of the face. The information ob-
tained in the relevant half seems to be sufficient for correctly
assigning an emotion. This provides opportunities for therapeu-
tic use in people with deficits in the area of emotion recognition.
A study conducted by Adolphs (2002) showed that the recogni-
tion accuracy of fear in patients with amygdala lesions could be
improved by prompting them to pay attention to the eyes of the
presented stimulus. Further research, such as eye-tracking stud-
ies, could contribute to the understanding of emotional facial
expression analysis.
Limitations
A limitation of the study concerns the methodological ap-
proach. Different times were chosen for the different emotions
in the dynamic presentation condition to ensure a natural proc-
ess. These time specifications, however, are based on the as-
sessment of subjects (Hoffmann et al., 2010). In this experiment
the participants were asked to assess dynamic sequences in
terms of their realistic representation. It was implicitly assumed
that these results reflect the sequence of actual emotion patterns
under natural conditions. From an epistemological perspective,
this link between perception and production of facial expres-
sions may not necessarily be present.
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