The purpose of this article is to shed light on the mechanism
and the procedures of a program that classifies an input face into
any of the six basic facial expressions, which are Anger, Disgust,
Fear, Happiness, Sadness and Surprise, in addition
to normal face.
This program works by apply PCA- principal component
analysis algorithm, which is applied of one side of the face, and
depends, on contrast to the traditional studies which rely on the
whole face, on three components: Eyebrows, Eyes and Mouth.
Those out-value are used to determine the facial feature array as
an input to the neural network, and the neural network is trained by
using the back-propagation algorithm. Note that the faces used in
this study belong to people from different ages and races.
Association Rules is an important field in Data Mining, which is
used to discover useful knowledge from a massive databases.
Association Rules have been used to extract the information from
the database transactions, and Apriori Algorithm is a pra
ctical
application for Association Rules and it is used to find frequent
itemsets from database transactions. In this paper, we present a
new improving on Apriori Algorithm by reduction generating of
candidate itemsets and this leads to improving efficiency Apriori
Algorithm.
