  The proposed framework in this paper has the primary objective of classifying
the facial expression shown by a person. These classifiable expressions can be
any one of the six universal emotions along with the neutral emotion. After the
initial facial localization is performed, facial landmark detection and feature
extraction are applied where in the landmarks are determined to be the fiducial
features: the eyebrows, eyes, nose and lips. This is primarily done using
state-of-the-art facial landmark detection algorithms as well as traditional
edge and corner point detection methods using Sobel filters and Shi Tomasi
corner point detection methods respectively. This leads to generation of input
feature vectors being formulated using Euclidean distances and trained into a
Multi-Layer Perceptron (MLP) neural network in order to classify the expression
being displayed. The results achieved have further dealt with higher uniformity
in certain emotions and the inherently subjective nature of expression.
