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The ability to process and identify facial emotions is an essential factor for an individuals social interaction. There are certain psychiatric disorders that can limit an individuals ability to recognize emotions in facial expressions. This problem could be confronted by making use of computational techniques in order to develop learning environments for the diagnosis, evaluation and training in identifying facial emotions. This paper presents an approach that uses image processing techniques, formal languages, anthropometry and Facial Action Coding System (FACS) to generate caricatures that represent facial movements related to neutral, satisfaction, sadness, anger, disgust, fear and surprise emotions. The rules that define the emotions were determined using an AND-OR graph to enable generating these images in a flexible manner. An evaluation conducted with healthy volunteers showed that some emotions are more easily recognized, while for other emotions the caricatures need to be further improved. This is a promising approach, since the parameters used provide flexibility to define the emotional intensity that must be represented.

The synthesis of facial expressions has applicationsin areas such as interactive games, biometrics systems, and training of people with disorders, among others. Although this is an area relatively well explored in the literature, there are no recent studies proposing to systematize an overview of research in the area. This systematic review analyzes the approaches to the synthesis of facial expressions in photographs, as well as important aspects of the synthesis process, such as preprocessing techniques, databases, and evaluation metrics. Forty-eight studies from three different scientific databases were analyzed. From these studies, we established an overview of the process, including all the stages used to synthesize expressions in facial images. We also analyze important aspects involved in these stages such as methods and techniques of each stage, databases, and evaluation metrics. We observed that machine learning approaches are the most widely used to synthesize expressions. Landmark identification, deformation, mapping, fusion, and training are common tasks considered in the approaches. We also found that few studies used metrics to evaluate the results, and most studies used public databases. Although the studies analyzed generated consistent and realistic results while preserving the identity of the subject, there are still research themes to be exploited.

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