|
|
 |
| |
|
|
|
Cross-Cultural Emotion Classification based on Incremental Learning and LBP-Features |
|
|
|
PP: 2651-2656 |
|
|
|
Author(s) |
|
|
|
M. Sultan Zia,
M. Arfan Jaffar,
|
|
|
|
Abstract |
|
|
| 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. |
|
|
|
|
|
|
|
