Classification Accuracy of Neural Networks with PCA in Emotion Recognition

Abstract

This paper presents classification accuracy of neural network with principal component analysis (PCA) for feature selections in emotion
recognition using facial expressions. Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and
classification applications. PCA is one of the popular methods used, and can be shown to be optimal using different optimality criteria. Experiment
results, in which we achieved a recognition rate of approximately 85% when testing six emotions on benchmark image data set, show that neural
networks with PCA is effective in emotion recognition using facial expressions.