Convolutional Neural Networks based Method for Improving Facial Expression Recognition
Recognizing facial expressions via algorithms has been a problematic mission among researchers from fields of science. Numerous methods of emotion recognition were previously proposed based on one scheme using one data set or using the data set as it is collected to evaluate the system without performing extra preprocessing steps such as data balancing process that is needed to enhance the generalization and increase the accuracy of the system. In this paper, a technique for recognizing facial expressions using different imbalanced data sets of facial expression is presented. The data is preprocessed, then, balanced, next, a technique for extracting significant features of face is implemented. Finally, the significant features are used as inputs to a classifier model. Four main classifier models are selected, namely; Decision Tree (DT) Multi Layer Perceptron (MLP) and Convolutional Neural Network (CNN) The Convolutional Neural Network is determined to produce the best recognition accuracy.