  Facial expression classification is a kind of image classification and it has
received much attention, in recent years. There are many approaches to solve
these problems with aiming to increase efficient classification. One of famous
suggestions is described as first step, project image to different spaces;
second step, in each of these spaces, images are classified into responsive
class and the last step, combine the above classified results into the final
result. The advantages of this approach are to reflect fulfill and multiform of
image classified. In this paper, we use 2D-PCA and its variants to project the
pattern or image into different spaces with different grouping strategies. Then
we develop a model which combines many Neural Networks applied for the last
step. This model evaluates the reliability of each space and gives the final
classification conclusion. Our model links many Neural Networks together, so we
call it Multi Artificial Neural Network (MANN). We apply our proposal model for
6 basic facial expressions on JAFFE database consisting 213 images posed by 10
Japanese female models.
