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Effects of Region Features on the Accuracy of Cross-database Facial Expression Recognition

Facial expression recognition (FER) in the context of machine learning refers to a solution whereby a computer vision system can be trained and used to automatically detect the emotion of a person from a presented facial image. FER presents a difficult image classification problem that has received increasing attention over recent years mainly due to the availability of powerful hardware for system implementation and the greater number of possible applications in everyday life. However, the FER problem has not yet been fully resolved, with the diversity of captured facial images from which the type of expression or emotion is to be detected being one of the main obstacles. Ready-made image databases have been compiled by researchers to train and test the developed FER algorithms. Most of the reported algorithms perform relatively well when trained and tested on a single-database but offer significantly inferior results when trained on one database and then tested using facial images from an entirely different database. This paper deals with the cross-database FER problem by proposing a novel approach which aggregates local region features from the eyes, nose and mouth and selects the optimal classification techniques for this specific aggregation. The conducted experiments show a substantial improvement in the recognition results when compared to similar cross-database tests reported in other works. This paper confirms the idea that, for images originating from different databases, focus should be given to specific regions while less attention is paid to the face in general and other facial sections.