Abstract
Facial expressions convey exhaustive information about human emotions and the most interactive way of social collaborations, despite differences in ethnicity, culture, and geography. Due to cultural differences, the variations in facial structure, facial appearance, and facial expression representation are the main challenges to the facial expression recognition system. These variations necessitate the need for multicultural facial expression analysis. This study presents several computational algorithms to handle these variations to get high expression recognition accuracy. We propose an artificial neural network-based ensemble classifier for multicultural facial expression analysis. The facial images from the Japanese female facial expression database, Taiwanese facial expression image database, and RadBoud faces database are combined to form a multi-culture facial expression dataset. The participants in the multicultural dataset originate from four ethnic regions including Japanese, Taiwanese, Caucasians, and Moroccans. Local binary pattern, uniform local binary pattern, and principal component analysis are applied for facial feature representation. Experimental results prove that facial expressions are innate and universal across all cultures with minor variations.
Highlights
The cultural variation in facial expression representation raises the issue of the universality of facial expressions
We developed the multi-culture facial expression database by combining the three different facial expression databases: Japanese female facial expression (JAFFE), Taiwanese facial expression image database (TFEID), and RadBoud face database (RaFD)
We developed a novel meta-learning technique, which is the fusion of NB classifier and Bernoulli distribution for well-organized cross-cultural facial expression which is classified by learning the presence of facial expression from a vast collection of artificial neural network ensemble collection (ANNE)
Summary
The cultural variation in facial expression representation raises the issue of the universality of facial expressions. In psychological and social science literature, the universality of human emotions in the appearance of facial expressions has remained one of the highest standing debates. In the late 1960s and early 1970s, researchers found evidence about the universality of facial expressions [1], [2]. Ekman identified basic emotions (i.e., happiness, anger, surprise, sadness, fear, and disgust) which are common for all human across different cultures [5]. We studied five expressions, such as anger, happiness, surprise, sadness, and fear for cross-cultural facial expression categorization.
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