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.
A simple and low cost thin layer chromatographic (TLC)-image analysis method was developed for rapid determination and quantification of monosodium glutamate (MSG) in some food samples. Chromatographic separation of MSG was achieved on silica gel TLC
plates, using n-butanol:glacial acetic acid:water (5:3:1, v/v/v) as the mobile phase and ninhydrin for spot detection. Image analysis of the scanned TLC plate was performed to quantify the amount of MSG, the method was validated and found to be accurate specific reliable and convenient for the analysis of MSG in some food sample.
The purpose of this article is to shed light on the mechanism
and the procedures of a neuro-fuzzy controller that classifies an
input face into any of the four facial expressions, which are
Happiness, Sadness, Anger and Fear. This program works
a
ccording to the facial characteristic points-FCP which is taken
from one side of the face, and depends, in contrast with some
traditional studies which rely on the whole face, on three
components: Eyebrows, Eyes and Mouth.
