A number of studies have shown that facial expression representations are cultural dependent and not universal. Most facial expression recognition (FER) systems use one or two datasets for training and same for testing and show good results. While their performance mortify radically when datasets from different cultures were presented. To keep high accuracy for a long time and for all cultures, a FER system should learn incrementally. We proposed a FER system that can offer incremental learning capability. Local Binary Pattern (LBP) Features are used for Region of Interest (ROI) extraction and classification. We used static images of facial expressions from different cultures for training and testing. The experiments on five different datasets using the incremental learning classification demonstrate promising results.
Sultan Zia, M. and Arfan Jaffar, M.
"Cross-Cultural Emotion Classification based on Incremental Learning and LBP-Features,"
Applied Mathematics & Information Sciences: Vol. 09:
5, Article 50.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss5/50