  It is argued that for the computer to be able to interact with humans, it
needs to have the communication skills of humans. One of these skills is the
ability to understand the emotional state of the person. This thesis describes
a neural network-based approach for emotion classification. We learn a
classifier that can recognize six basic emotions with an average accuracy of
77% over the Cohn-Kanade database. The novelty of this work is that instead of
empirically selecting the parameters of the neural network, i.e. the learning
rate, activation function parameter, momentum number, the number of nodes in
one layer, etc. we developed a strategy that can automatically select
comparatively better combination of these parameters. We also introduce another
way to perform back propagation. Instead of using the partial differential of
the error function, we use optimal algorithm; namely Powell's direction set to
minimize the error function. We were also interested in construction an
authentic emotion databases. This is a very important task because nowadays
there is no such database available. Finally, we perform several experiments
and show that our neural network approach can be successfully used for emotion
recognition.
