Facial Expression Analysis - A Hybrid Neural Network Based Approach

It is argued that for the computer to be able to interact with humans, it needs to have human communication skills. One of these skills is the ability to understand the emotional state of human. This paper describes neural network based approaches for emotion classification. We learn a classifier that can recognize 6 basic emotions with an average accuracy of 83% 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, 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 error function to adjust the weights of a neural network, we use optimal algorithms such as Powell algorithm to minimize the error function. We also perform several experiments and show that our hybrid neural network approach can be successfully used for emotion recognition.