Review of Different Combinations of Facial Expression Recognition System

The facial expression recognition (FER) system is a classifier system that attempts to recognize facial expressions based on the analysis of emotion behaviour on the face. The FER system can be implemented by using one classifier or combining multi feature extraction and/or multi classifiers. In general, FER is used with one classifier system to find the best label. Although a classification system is commonly used to find the most likely facial expression, it still produces substantial numbers of errors due to several factors that influence the FER result, such as data quantity, and environmental conditions (i.e. illumination and noise). Therefore, combined multi feature extraction methods and/or multi classifier systems are useful to avoid the single classifier errors. Multi feature extraction or a multi classifier system combination are used to take advantage of different system hypotheses to find an accurate result. This paper is a survey of the latest system combination techniques being used to enhance the classification performance in the FER system; the most recent studies are presented.