A principal component analysis of facial expressions.
Pictures of facial expressions from the Ekman and Friesen set (Ekman, P., Friesen, W. V., (1976). Pictures of facial affect. Palo Alto, California: Consulting Psychologists Press) were submitted to a principal component analysis (PCA) of their pixel intensities. The output of the PCA was submitted to a series of linear discriminant analyses which revealed three principal findings: (1) a PCA-based system can support facial expression recognition, (2) continuous two-dimensional models of emotion (e.g. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-1178) are reflected in the statistical structure of the Ekman and Friesen facial expressions, and (3) components for coding facial expression information are largely different to components for facial identity information. The implications for models of face processing are discussed.
Calder
A J
AJ
MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 2EF, Cambridge, UK. andy.calder@mrc-cbu.cam.ac.uk
Burton
A M
AM
Miller
P
P
Young
A W
AW
Akamatsu
S
S
D000293:Adolescent; D000328:Adult; D003463:Cues; D016002:Discriminant Analysis; D004644:Emotions / Q000502:physiology; D005149:Facial Expression; D005260:Female; D005556:Form Perception / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008568:Memory / Q000502:physiology