Emotion Recognition Using Facial Expressions

Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine learning can help automate this complicated task with the help of feature engineering. This work proposes some pipelines trained on the JAFFE dataset using feature extraction methods, namely principal component analysis (PCA) and local binary pattern (LBP) combined with Fisher discriminant ratio (FDR) as a feature selection method. In order to build a classification scheme capable of successfully identifying face images related to the six universal emotions and neutral expression, all possible combinations have been empirically analyzed. In the final model, PCA combined with FDR has been used on the support vector machine classifier with a linear kernel. The results obtained are encouraging and this work may also prove important for disciplines other than computer science such as for management purposes.