M. Nakano, Y. Mitsukura, M. Fukumi, and N. Akamatsu (Japan)
Smiles recognition, Simple principal component analysis, Neural networks, Recognition of facial expressions
The concern about man-machine interface has increased in
recent years, and is expecting application of the recognition
of facial expressions. As one of the face expressing method
for discriminating front faces, there is a method of com
pressing dimensions of the feature vector into low dimen
sions by using the principal component analysis (PCA).
In this paper, the simple principal component analysis
(SPCA) is applied to dimensionality compression of por
tions that constitute a face, which is a data-oriented fast
method. An angle (cos θ) is calculated using the eigenvec
tor and the gray scale image vector of each picture pattern.
By using the value of cos θ, similarity between true and
false (plastic) smiles is clariﬁed and the true smile is dis
criminated. Finally, in order to demonstrate the effective
ness of the proposed face smile or false classifying method,
computer simulations are done.