Image preprocessing techniques for facial expression classification - Pereira, Andre Luiz da S.; Fernandes, Leandro A. F.; Conci, Aura (Proceedings of the Life Improvement in Quality by Ubiquitous Experiences Workshop (LIQUE), 2022)
Facial expression classification is an essential aspect of human interaction, and its proper classification is considered fundamental for the future inclusion of multimedia psychology into human-computer interfaces. For this purpose, this article aims to identify a preprocessing pipeline capable of reduce the accuracy variance for facial expression classification. Thereunto, Extended Cohn-Kanade Dataset, Support Vector Machine, and Bag of Features were used in six pipelines. Compared to a pipeline with minimal preprocessing, the best results presented an accuracy variance reduction of 65.63%.