Analysis of in- and out-group differences between Western and East-Asian facial expression recognition

Recent cross-cultural studies have questioned the cultural universality of facial expressions from a psychological viewpoint. However, the automatic facial expression recognition (FER) systems are based on the assumption that facial expressions are the same for all human beings, excluding the differences that may appear between different races and cultures. Therefore, this paper presents an analysis of culturally specific facial expression recognition focused on Western and East-Asian expressive faces using an automatic FER system based on 3 different feature extraction methods (appearance-, geometric-, and a proposed hybrid-based). Our study is focused on 4 specific facial regions (eyes-eyebrows, mouth, nose and forehead/outline) and it is powered by Principal Component Analysis (PCA) which enables the visual examination of the most relevant differences among the 6 basic expressions from each racial group. Feature extraction methods are evaluated using Support Vector Machines (SVM) and 4 standard databases. In addition, our findings are compared with a cross-cultural human study applied to 40 participants from both racial groups.