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idiosyncratic variant, is a well identifiable specific usage of some facial signal. Such
cultural dialect can be seen as an in-group advantage in human facial expression
recognition, whereby human facial expression recognition is generally more accurate
for perceivers from the same cultural group as expressers. These works all have been
using photographs, where the cultural identity of the face was clear. Ruttkay [6]
discusses further empirical findings and provides a detailed scheme of the possible
factors of cultural differences in interpreting facial emotions of virtual agents.
When designing virtual agents, it is a challenging and not very much exploited
possibility to use non-realistic faces. There are two motivations for going for cartoon-
like faces. On the one hand, it has been shown that the more realistic the design is, the
more critical the human perceivers are. As the realism increases, the “uncanny valley”
effect occurs [7, 8]. However, in most application contexts it is the “suspension of
disbelief” which is to be achieved, not the full realism. Further on, when using non-
realistic faces there are additional means of expressivity (exaggeration, usage of non-
realistic features or additional signals). Hence it is interesting to study possible
cultural variations in perception of cartoon-like faces. Do findings from psychology
on interpreting realistic facial expressions carry over to cartoon-like faces? How do
the drawing style and familiarity with non-realistic facial expressions (e.g. in the
tradition of comics) influence the interpretations of cartoon-like facial expressions?
Koda's cross-cultural study [9] on the recognition of cartoon-like agent facial
expressions drawn by Asian and Western designers suggests that the recognition
accuracy of facial expressions is higher for virtual agents designed by the same
cultural group as the subjects. E.g. Japanese cartoon-like facial expressions are
recognized most accurately by Japanese, and Western cartoon-like facial expressions
are recognized most accurately by western countries. The results suggest an in-group
advantage is also applicable to cartoon-like facial expressions of virtual agents.
Recent psychological study also investigates the cultural differences of facial
expressions by focusing on the facial regions. Research on human eye movements to
interpret photo realistic human facial expressions showed East Asian participants
mostly focused on the eyes, and Western participants scanned the whole face [10].
Yuki et al. used pictograms and photorealistic human facial images and suggest that
Americans tend to interpret emotions based on the mouth, while Japanese tend to
focus on the eyes [11]. Yuki et al. state this cultural difference arises from cultural
norms: that people in cultures where emotional subduction is the norm (such as Japan)
would focus on the eyes, and those in cultures where overt emotional expression is the
norm (such as U.S.) would focus on the mouth shape.
This study applies the findings of [10, 11] to animated cartoon-like virtual agent
faces to improve the culturally effective facial expression design of virtual agents.
Such findings can be used not only to derive design guidelines when aiming at users
of a single culture, but as adaptation strategies in applications with multicultural
users. E.g. in an ATM machine or on-line shop, if the user's cultural identity is
established; the virtual agent's facial expressions may be fine-tuned for optimal
recognition for the given culture. Animating virtual agents' facial expression is rather
easily done, but much more difficult in case of physical robots. Recent social robots
have cartoonish faces with limited facial expressions, e.g., Kismet [12], Nexi MDS
