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: Proceedings of the 2nd Life Improvement in Quality by Ubiquitous Experiences WorkshopDO - 10.5753/lique.2022.19995, 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%.