Recognition Profile of Emotions in Natural and Virtual Faces

BACKGROUND: Computer-generated virtual faces become increasingly realistic including the simulation of emotional expressions. These faces can be used as well-controlled, realistic and dynamic stimuli in emotion research. However, the validity of virtual facial expressions in comparison to natural emotion displays still needs to be shown for the different emotions and different age groups. METHODOLOGY/PRINCIPAL FINDINGS: Thirty-two healthy volunteers between the age of 20 and 60 rated pictures of natural human faces and faces of virtual characters (avatars) with respect to the expressed emotions: happiness, sadness, anger, fear, disgust, and neutral. Results indicate that virtual emotions were recognized comparable to natural ones. Recognition differences in virtual and natural faces depended on specific emotions: whereas disgust was difficult to convey with the current avatar technology, virtual sadness and fear achieved better recognition results than natural faces. Furthermore, emotion recognition rates decreased for virtual but not natural faces in participants over the age of 40. This specific age effect suggests that media exposure has an influence on emotion recognition. CONCLUSIONS/SIGNIFICANCE: Virtual and natural facial displays of emotion may be equally effective. Improved technology (e.g. better modelling of the naso-labial area) may lead to even better results as compared to trained actors. Due to the ease with which virtual human faces can be animated and manipulated, validated artificial emotional expressions will be of major relevance in future research and therapeutic applications

Demographic information on the experimental groups.

<p>MWT-B, Mehrfachwahl Wortschatz Intelligenztest (vocabulary intelligence test); SD, standard deviation; PANAS, Positive and Negative Affect Scale.</p

FACS of virtual emotions: percentage of faces with respective AU present and mean intensity.

<p>FACS, Facial Action Coding System; AU, action unit.</p

Accuracy rates in % for best recognized faces.

a<p>best recognized face.</p

Examples of virtual emotion: fear expression in one male and one female character.

<p>Examples of virtual emotion: fear expression in one male and one female character.</p

Figure 1

<p>A: Recognition accuracy (chance performance was 16.67%) and B: response times with standard error of mean for natural and virtual facial expressions across all subjects.</p

Accuracy ratings and confusions (% correct) for virtual and natural faces.

<p>Boldface indicates recognition rates of intended emotion.</p

Mean subjective intensity ratings for virtual and natural faces (on a scale from 1-not intense to 6-very intense).

<p>SD, standard deviation; Min, Minimun; Max, Maximum.</p

Recognition accuracy with standard error of mean for natural and virtual facial expressions in subjects under and above the age of 40 (chance performance was 16.67%).

<p>Recognition accuracy with standard error of mean for natural and virtual facial expressions in subjects under and above the age of 40 (chance performance was 16.67%).</p